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In this article they exemplify how Google's search engine algorithm shows first news media with a particular ideological view of a complex conflict, making a disproportional advantage towards a perspective prone to justify more violence.

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Unrest in Bangladesh is revealing the bias at the heart of Google’s search engine

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Google’s search engine fields 6.3 million queries every second. But the results it dishes up are far from perfect.

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A recent article on AI bias published in an English-language newspaper is Sky News' report titled "How AI is driving a 'distortion of reality' when it comes to how women are presented online." The article, released on October 8, 2025, highlights how AI can distort reality when representing women online. Researchers generated 40,000 CVs and found that AI assumed women were younger and less experienced, while older men were ranked as more qualified; moreover, less than 1% of AI responses showed systematic biases according to OpenAI's research.

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How AI is driving a ‘distortion of reality’ when it comes to how women are presented online

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Researchers generated 40,000 CVs and found AI assumed women were younger and less experienced while rating older male applicants as more qualified.

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This article is very clearly about how human bias leads directly to algorithmic bias. What surprised me is how aware police officers are of the issue. This is actually positive - they want to be doing good work, but they are seeing a "feedback loop" that is created by biased inputs (training data) that creates biased outputs (e.g. more policing in areas already subject to human bias) that result in further inputs (more arrests in that same area). The loop then repeats. Rather than intelligently distributing resources, police are spending inordinate amounts of time and money in "hot spots" that, in some ways, are "hot" simply because the algorithm tells them to be there all the time.

Far and away the worst aspect of predictive policing is individual risk assessments where target variables (like, for example, recidivism) are determined by class labels that mask clear bias. The example often cited here is the question "Have your parents ever been arrested?" In the US, you're far more likely to answer that if you're poor, black, or a minority. The result? You're considered likely to offend or reoffend, and you're watched more carefully than someone who may actually be at greater risk to reoffend.

Systems that are meant to spread policing and resources more efficiently are simply exacerbating existing problems. It's sad, and quite simply, terrifying.

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Predictive policing - the use of machine-learning algorithms to fight crime - risks unfairly discriminating against protected characteristics including race, sexuality and age, a security thinktank has warned.

Such algorithms, used to mine insights from data collected by police, are currently deployed for various purposes including facial recognition, mobile phone data extraction, social media analysis, predictive crime mapping and individual risk assessment.

Researchers at the Royal United Services Institute (RUSI), commissioned by the government's Centre for Data Ethics and Innovation, focused on predictive crime mapping and individual risk assessment and found algorithms that are trained on police data may replicate - and in some cases amplify - the existing biases inherent in the data set, such as over- or under-policing of certain communities.

"The effects of a biased sample could be amplified by algorithmic predictions via a feedback loop, whereby future policing is predicted, not future crime," the authors said.

The paper reveals that police officers, who were interviewed for the research, are concerned about the lack of safeguards and oversight regarding the use of predictive policing.

One officer told the researchers that "young black men are more likely to be stop and searched than young white men, and that's purely down to human bias. That human bias is then introduced into the data sets, and bias is then generated in the outcomes of the application of those data sets."

Another officer said police forces "pile loads of resources into a certain area and it becomes a self-fulfilling prophecy, purely because there's more policing going into that area, not necessarily because of discrimination on the part of officers".

The technological landscape was described by one officer as a "patchwork quilt, uncoordinated and delivered to different standards in different settings and for different outcomes".

The briefing paper identifies individuals from disadvantaged socioeconomic backgrounds who are "calculated as posing a greater risk" of criminal behaviour by algorithms.

This bias exists because individuals from this group are more likely to have frequent contact with public services and, in doing so, generate higher levels of data, which the police often have access to, the paper reveals.

The implications are serious both in terms of police allocation of resources, which may be ineffective as they are based on incorrect calculations, and legally where "discrimination claims could be brought by individuals scored 'negatively' in comparison to others of different ages or genders", the paper adds.

The briefing paper also highlights the risk of "automation bias", whereby police officers become overreliant on the use of analytical tools, undermining their discretion and causing them to disregard other relevant factors.

The paper, Data Analytics and Algorithmic Bias in Policing, by Alexander Babuta and Marion Oswald, summarises the interim findings of an ongoing independent study into the use of data analytics for policing within England and Wales and explores different types of bias that can arise.
Topics

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Predictive policing poses discrimination risk, thinktank warns

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Predictive policing - the use of machine-learning algorithms to fight crime - risks unfairly discriminating against protected characteristics including race, sexuality and age, a security thinktank has warned. Such algorithms, used to mine insights from data collected by police, are currently deployed for various purposes including facial recognition, mobile phone data extraction, social media analysis, predictive crime mapping and individual risk assessment.

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Created on May 26, 2025                                 Predictive algorithms can be biased.  

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After studying the types of bias discussed in *Big Data’s Disparate Impact*, I understand that the UK Ministry of Justice’s predictive algorithm raises critical concerns due to three key forms of algorithmic bias that risk undermining fairness and justice. First, **feature selection bias** occurs when the system uses personal trauma—such as mental health history or experiences of domestic abuse—as indicators of future criminality, unfairly penalizing vulnerable individuals. Second, **proxy bias** emerges when seemingly neutral data serves as an indirect stand-in for sensitive attributes like race, class, or social status, leading to discriminatory outcomes. Third, **labeling bias** arises from the use of historical crime data that may already reflect systemic prejudice, which the algorithm then learns and perpetuates. After studying these types of bias, I recognize how predictive technologies, if not carefully regulated, can replicate and amplify existing social inequalities. This underscores the urgent need for transparency, accountability, and ethical scrutiny in the deployment of such systems within the justice sector.

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Intelligence-led policing - possible biasing

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Intelligence-led policing - possible biasing

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The implementation of the AI driven tool developed by the police department of Pasco County, Florida, it is called “intelligence-led policing” program.  According to the data mining mechanisms described in the Barocas & Selbst's article, in the Pasco example several mechanisms are presented:
1) Defining the “Target Variable” and “Class Labels”: a young student was visited by the police given the results of the AI tool, this guy havet had a criminal record before but its place of residence targeted him as potential criminal. Usually, the definition of Class Labels is a binary process (yes or no) so he felt in the Class of potential criminal,

2) Training data / Data Collection: Police have used historical crime records which happen historically in neighbourhoods where crime commonly happened, thus skewing the data. Simply, police have no more data to set as input in the model

3) Proxies: mainly because the student is of black race, some proxy relating to another characteristic may have caused him to fall into a high potential criminal zone, given that the AI tools are biased against Black or Asian races

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Predictive policing AI is on the rise − making it accountable to the public could curb its harmful effects

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AI that anticipates where crimes are likely to occur and who might commit them has a troubling track record. Democratic accountability could shine a light on the technology and how it’s used.

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Well after those lessons now I can claim that almost everything a statistical bias, as most of the policing hot spots and assumptions are based on some algorithims and means, which is usually hurtfull and discriminatory.

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The LAPD case is a multi-layered example of algorithmic bias. It involves biased training data, problematic target definitions, and the use of features that act as proxies for protected attributes. Although perhaps not designed to be discriminatory, the system replicated and legitimized inequalities, leading to unfair targeting and loss of public trust. This underscores the importance of transparency, accountability, and careful problem formulation when using AI in public policy.

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Is the controversy to do with algorithmic bias?

Which type of bias is involved?

The controversy revolves around algorithmic bias, and multiple biases are involved. There is historical, sampling, measurement, labeling and social biases involved. This is a very nuanced and multilayered issue involving data, design and human nature and society. Technical awareness, ethical reflection, diverse voices and accountability are needed to address these issues.

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Considering the very racist history of the LAPD that is still ongoing, the fact that the predictive policing algorithm included biases of sampling, proxy and labelling is really no surprise. It is yet another example of the systemically racist aspect of policing in the USA - an algorithm will always reflect the conscious and subconscious biases of those who created it.

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Is the controversy to do with algorithmic bias? What kind of bias is involved?

Yes it most probably is, because the algorithms guiding the data processing will have been skewed certain ways in how they come to their conclusions. But other types of bias are involved including historical bias, measurement bias (including bias by proxy, labelling bias) and very importantly the particular attitude of the human ultimately making the decision on that day. Because in the case of benefits processing in the UK, which is the article I reviewed, the human still has the final say in the process. Rubrics can only get us so far in making a less biased decision, and humans will always be biased (as will the machines they program). What we can do is be aware of our bias, be aware of the bias we are programming in to tech, train it on more representative (and ethically sourced) data sets to reduce this bias, and where relevant add counter-bias (as companies like Google have done with their AI, sometimes to negative results).

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Unconscious Expectations in Digital Interactions

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Unconscious Expectations in Digital Interactions

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The issue I experienced wasn't really about a technical glitch but more about how people expect technology to behave. For example, when a chatbot doesn't respond the way we want, we label it as “broken” instead of questioning our own assumptions. I’d say it reflects a deeper bias in society — we expect machines to follow human logic, yet forget they’re built by humans with biases. It's less about malfunction and more about unrealistic expectations shaped by our own cultural lens (anthropocentric thinking over systemic understanding).

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I previously shared the story of someone being arrested because the police held suspicions on them, on the sole basis of AI algorithms.

With little information on the case, it's hard to know which type of bias was involved. What was most striking was the fact the police offers trusted the results of the AI solution, without exercising any critical thinking (maybe because they were happy with what came out of it?).

But we could easily think the solution was biased, in multiple ways. (1) Training data: we know that some populations have been discriminated by the police in the past. Using historical data sets as they are is likely to lead to an over representation of those populations in people to watch out for. (2) Feature selection: the AI algorithms often use data sets that are linked to where people live, their social economic status, etc. Some of these data sets are proxies for ethnicity. Eventually, it can lead to ethnicity discrimination.

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Quite often when going grocery shopping, I tend to use self-checkout at my local supermarket. When purchasing alcohol on numerous occasions (after getting an ID check), the theft detection camera often flags the action of reaching into my pocket to grab my wallet and returning my ID as a theft behavior. This is quite easy to see as the recorded action is played on the screen after the machine freezes the checkout process. I found that this happens regularly more than likely because the average shopper is not going to reach into their pocket for a wallet and/or purse more than once, hence flagging the abnormal action as "theft". A very small, but consistent and notable bias in the programming of the detection system.

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In the case of policing or sentencing controversies, yes, the issue is often related to algorithmic bias. These algorithms are typically used to predict recidivism, risk, or likelihood of reoffending, but they can unintentionally reinforce existing biases in the criminal justice system.

The type of bias involved is discriminatory bias, specifically historical bias and measurement bias. Historical bias occurs because these algorithms are often trained on historical data that reflects past inequalities and discriminatory practices, such as racial profiling or disproportionate sentencing of minority groups. Measurement bias may arise when the data used to train these algorithms includes biased variables, like arrest records or prior convictions, which can unfairly target certain demographics.

As a result, these biases can perpetuate unfair outcomes in policing and sentencing, with certain groups being unfairly over-policed or over-sentenced.

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A significant part of the ‘welfare robots’ controversy involves algorithmic bias. AI systems in welfare often rely on historical data to make decisions, such as determining eligibility for benefits or identifying potential fraud. If this data reflects existing inequalities or systemic biases (e.g., socioeconomic disparities, racial discrimination, or geographical inequities), the algorithm may perpetuate or even exacerbate these biases. This is particularly concerning in welfare systems, where marginalized groups are disproportionately affected.

Statistical Bias arises if the training data is unrepresentative or incomplete. For example, if the algorithm is trained on data that does not reflect the diversity of welfare recipients, it may systematically disadvantage certain groups.

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The false belief in freedom has come to disrespect others. When social networks are thought of as a space to publish "everything", there are no filters and that has led to an increase in biases, social division and increased anxiety or depression.

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This controversy is related to algorithmic bias because as mentioned with the proxy bias even if there is no label related to race other labels are closely tight to this factor and in our society where races/ ethnicities impact a lot your everyday. I highly recommend these two books on the subject: Brayne, Sarah (2021). Predict and surveil. Data,

discretion, and the future of policing. New York,

NY: Oxford University Press.

Jefferson, Brian Jordan (2020). Digitize and

punish. Racial criminalization in the digital

age. Minneapolis: University of Minnesota

Press.

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The police department's hot spot policing program exhibits multiple types of bias, especially bias by proxy as it disproportionately targeted areas with "the lousiest socioeconomic parts of the city." These areas also happened to be neighborhoods with the highest percentage of minority residents. While the algorithm itself used calls for service data rather than arrest data to avoid direct feedback loops, the geographic focus effectively created a proxy for race and class. This has led to increased police presence in already marginalized communities. The controversy stems from both algorithmic bias and systemic bias, as the underlying zoning patterns that concentrate poverty and minorities in certain areas became encoded into the predictive policing model's outputs, perpetuating historical patterns of over-policing in these communities

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The evolution of ‘Hot Spot’ policing in Manchester - Manchester Ink Link

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Police initiatives to use data to tackle high-crime areas have shown mixed results.

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The controversy surrounding predictive policing and sentencing algorithms clearly illustrates algorithmic bias, specifically through sampling, label, and proxy biases. These algorithms often use historical data that reflects societal inequalities, such as higher incarceration rates among minority groups. This data can inadvertently lead to biased outcomes, as the algorithm learns from flawed or prejudiced labels and categories. For example, if an algorithm uses factors like zip codes or criminal records, which may correlate with race, it risks perpetuating racial disparities in sentencing and policing. Addressing these biases is crucial to ensure fairer and more equitable use of technology in justice systems.

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Although it is true that humans are biased and that they are the ones who design AI systems, we anticipate that AI will be objective.
The elimination of prejudice in artificial intelligence is not only a technical difficulty but also a cultural necessity. When it comes to ensuring that artificial intelligence systems make a beneficial contribution to society, it is necessary to collaborate across different fields, maintain constant monitoring, and make a commitment to ethical values. If this can be fully achieved? Maybe in the future

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Unfortunately, I believe it is almost (if not completely) impossible for algorithmic bias to be eliminated. Analysing these systems rigorously and constantly having humans check over what is done is the only way that we can ensure these systems don't amplify present bias particularly within minority groups (whether this be to do with race, gender, or any other protected characteristic). I believe there is a time and a place to use these systems, but in contexts such as reading resumes etc. human input is needed even within early stages.

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I don’t have social media, but I would like to contribute to the topic. Analyzing algorithmic bias in policing and sentencing models is crucial, as these systems can amplify preexisting inequalities, disproportionately penalizing minority groups. Evaluating and mitigating these biases is essential to promoting fairness and equity in public security systems.

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I have done a little test by using a generic prompt to generate the image of "high rank executive sitting in his Manhattan office, having the view of city landscape" This generic prompt always has as an output a white male executive. It doesn't matter which image generator I used. If I change the prompt and ask for the image of a Jamaican executive, then it will give me the image of a black male executive. it will never give me an image of a woman, and we all know we do have female executive in high rank positions, but then I would need to be very detail oriented in my prompt to include race and gender, even age to have a result that would portrait a female executive.

the answers as of why they provide me those results with the generic prompts are that the data is feed with how the media and other outlets represent a high rank executive.

The issue is not the technology, we just need to keep refining the data to be more representative of the society we want. With policing the problem starts when we assume that all individual in certain demographic or ethnic group are criminals, just like the image generators assume that all high rank executives are white males.

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It is almost as if for the AI to be unbiased then it would need to be reviewed by everyone as we all have our biases we are unaware of, and we will never be able to do this. One could also argue that AI is less bias as it is created through a compilation of multiple perspectives through the data it is receiving, whereas if it was an individual making these decisions that individual would be bias (as we all are in some shape of form) and therefore be more harm than the AI which would technically have a more balanced opinion in the end.

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Is the controversy related to algorithmic bias?

Yes, the controversies surrounding the policing systems mentioned in the image are closely linked to algorithmic bias. Many modern policing systems utilize data-driven algorithms to predict criminal activity and allocate resources based on historical data pertaining to specific areas or groups. This approach may inadvertently exacerbate existing biases.

For instance, these systems might over-police certain neighborhoods predominantly inhabited by minority groups while neglecting other areas that may equally require attention. This exemplifies a typical issue of algorithmic bias, particularly when models are constructed on unbalanced or biased training datasets, potentially amplifying such unfair phenomena.

What types of bias are involved? According to Barocas and Selbst's classification, several types of biases may be relevant here:

Historical Bias: When a model relies on datasets imbued with historical prejudices, injustices related to race, gender, etc., can persist through the algorithm. For example, certain groups may have been disproportionately monitored or arrested due to racial profiling in history; consequently, algorithms built upon such data continue labeling these groups as high-risk individuals or areas.

Representation Bias: Certain populations may be either overrepresented or underrepresented within the dataset leading to inaccurate predictions for those groups by the algorithm. For instance, higher arrest rates in particular regions do not necessarily indicate elevated crime rates but rather reflect concentrated law enforcement efforts in those areas which inflate risk assessments for minority communities.

Measurement Bias: The metrics or variables employed within an algorithm might not be equitable. Arrest records or crime reports could be influenced by racial prejudice resulting in misleadingly high crime rates attributed to specific ethnicities; this leads algorithms towards unjust predictions.

Reflecting on target assessment: By integrating Baracas and Selbst's analysis into our understanding, we recognize that there is a crucial need for vigilance against introducing biases during system design and for ongoing iterations and improvements aimed at preventing systemic injustices from arising.

#DataEthicsMOOC @UoE_EFI

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The definition of bias has evolved over time. Now, it is defined as a negative exclusion. It is the issue of exclusion and perpetuation of systemic injustice that is correctly at the center of ethics and AI. However, is ALL bias bad? For example, some people have a purchasing bias (still a valid term) for Coke versus Pepsi, but this might be considered neutral. I am curious how programmers can train models that continually weed out negative exclusion bias

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Reading through all the below I dare to say that every algorithm (and system in which it is implemented) carries bias. There are many steps in the process where bias can sneak into the system (programming, data collection, data weighing, user input, result presentation, etc.) and it will sneak in. One can only try hard to iron out as much as possible (the stuff we recognize) but some bias unfortunstely will probably always remain and slip through our scrutiny. One needs to apply best efforts though to catch as much as possible.

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How technology is allowing police to predict where and when crime will happen

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How technology is allowing police to predict where and when crime will happen

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In this example, the controversy is around the algorithmic bias resulting from the dataset used to train the model: "by relying on past data to create the software, the program had itself “learned” racism and bias, and would continue reinforcing it even if police forces and wider society progresses"

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How big data can now be used to predict where crime will happen

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Report finds that British police have wealth of data but ‘lack capability to use it’

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Historical bias in Argentina's Artificial Intelligence Applied to Security Unit 

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Historical bias in Argentina's Artificial Intelligence Applied to Security Unit 

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The controversy surrounding Argentina's Ministry of Security and its Artificial Intelligence Applied to Security Unit (UIAAS) stems from the risk that AI might disproportionately target certain groups, especially marginalized communities, due to biased data or flawed algorithms. The primary concern is that the system, if trained on historical policing data, could inherit existing (historical) biases from that data, resulting in discriminatory practices like racial profiling or unequal treatment of specific populations.

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I believe 2 factors should be more involved; 1 is what is the goal. If its to deter crime or prevent it from happening, white or blue collar, then developing a system that includes actual factors, like race and criminal history should be part of that, just better. If more white collar crimes happen in 'white' areas, then it makes sense to direct more policing in that area. Same if other crimes occur in black or brown areas. If less crime is the goal, then maybe the labels and variables as some of the criteria, do work. If they don't, then change them. If the results fail vs. the goal. Change them. 2. Ultimately, its a tool. Not a weapon. When we accept that a tool to help deter crime needs to be continually honed then seems to me we'd want to welcome a tool, AND its interations. These discoveries of latent bias can also be an indicator that a tool may not be ready for prime time. XAI and full transparency can head some of this off, vs. the pressure from software companies to beat competitors to the system used by the police. Especially when it made clear in the Barocas and Selbst paper that bias or prejuidice is not intended.

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I posted about predictive policing in the Olympics. It's exactly about algorithmic bias. It is likely perpetuated by sampling biases in the data.

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Olympics’ AI Security Stokes Backlash Over Mass Surveillance

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The Paris Summer Olympics that begin Friday include among the most public and controversial rollouts ever of algorithmic video surveillance, an AI event-security technology that uses machine learning to analyze video footage in real time to detect—and even predict—threats and other anomalies.

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The Computer Got It Wrong': How Facial Recognition Led To False Arrest Of Black Man

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The Computer Got It Wrong': How Facial Recognition Led To False Arrest Of Black Man

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This article is an example of a person of colour being incorrectly arrested for a crime he didn't commit, due to a false positive by facial recognition technology.

It is an example of sampling bias in data, similar to that demonstrated in the 'gender shades' example from the MIT researcher. It could also be an example of inaccurate labelling. This produced the troubling result of all black faces 'appearing the same' to the facial recognition technology.

What I found equally troubling about the article is the apparent lack of questioning about potential motives or alibis before the arrest, indicating the blind trust and faith placed in the system. The willingness of law enforcement to trust the system indicates racist undertones and reflects broader societal inequalities.

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Police in Detroit were trying to figure out who stole five watches from a Shinola retail store. Authorities say the thief took off with an estimated $3,800 worth of merchandise.

Investigators pulled a security video that had recorded the incident. Detectives zoomed in on the grainy footage and ran the person who appeared to be the suspect through facial recognition software.

A hit came back: Robert Julian-Borchak Williams, 42, of Farmington Hills, Mich., about 25 miles northwest of Detroit.

In January, police pulled up to Williams' home and arrested him while he stood on his front lawn in front of his wife and two daughters, ages 2 and 5, who cried as they watched their father being placed in the patrol car.

His wife, Melissa Williams, wanted to know where police were taking her husband.

" 'Google it,' " she recalls an officer telling her.

Robert Williams was led to an interrogation room, and police put three photos in front of him: Two photos taken from the surveillance camera in the store and a photo of Williams' state-issued driver's license.

"When I look at the picture of the guy, I just see a big Black guy. I don't see a resemblance. I don't think he looks like me at all," Williams said in an interview with NPR.

"[The detective] flips the third page over and says, 'So I guess the computer got it wrong, too.' And I said, 'Well, that's me,' pointing at a picture of my previous driver's license," Williams said of the interrogation with detectives. " 'But that guy's not me,' " he said, referring to the other photographs.

"I picked it up and held it to my face and told him, 'I hope you don't think all Black people look alike,' " Williams said.

Williams was detained for 30 hours and then released on bail until a court hearing on the case, his lawyers say.

At the hearing, a Wayne County prosecutor announced that the charges against Williams were being dropped due to insufficient evidence.

Civil rights experts say Williams is the first documented example in the U.S. of someone being wrongfully arrested based on a false hit produced by facial recognition technology.
Lawyer: Artificial intelligence 'framed and informed everything'
What makes Williams' case extraordinary is that police admitted that facial recognition technology, conducted by Michigan State Police in a crime lab at the request of the Detroit Police Department, prompted the arrest, according to charging documents reviewed by NPR.

The pursuit of Williams as a possible suspect came despite repeated claims by him and his lawyers that the match generated by artificial intelligence was faulty.

The alleged suspect in the security camera image was wearing a red St. Louis Cardinals hat. Williams, a Detroit native, said he would under no circumstances be wearing that hat.

"They never even asked him any questions before arresting him. They never asked him if he had an alibi. They never asked if he had a red Cardinals hat. They never asked him where he was that day," said lawyer Phil Mayor with the ACLU of Michigan.

On Wednesday, the ACLU of Michigan filed a complaint against the Detroit Police Department asking that police stop using the software in investigations.

In a statement to NPR, the Detroit Police Department said after the Williams case, the department enacted new rules. Now, only still photos, not security footage, can be used for facial recognition. And it is now used only in the case of violent crimes.

"Facial recognition software is an investigative tool that is used to generate leads only. Additional investigative work, corroborating evidence and probable cause are required before an arrest can be made," Detroit Police Department Sgt. Nicole Kirkwood said in a statement.

In Williams' case, police had asked the store security guard, who had not witnessed the robbery, to pick the suspect out of a photo lineup based on the footage, and the security guard selected Williams.

Victoria Burton-Harris, Williams' lawyer, said in an interview that she is skeptical that investigators used the facial recognition software as only one of several possible leads.

"When that technology picked my client's face out, from there, it framed and informed everything that officers did subsequently," Burton-Harris said.

Academic and government studies have demonstrated that facial recognition systems misidentify people of color more often than white people.

One of the leading studies on bias in face recognition was conducted by Joy Buolamwini, an MIT researcher and founder of the Algorithmic Justice League.

"This egregious mismatch shows just one of the dangers of facial recognition technology which has already been shown in study after study to fail people of color, people with dark skin more than white counterparts generally speaking," Buolamwini said.

"The threats to civil liberties posed by mass surveillance are too high a price," she said. "You cannot erase the experience of 30 hours detained, the memories of children seeing their father arrested, or the stigma of being labeled criminal."

Maria Miller, a spokeswoman for the prosecutor's office, said the case was dismissed over insufficient evidence, including that the charges were filed without the support of any live witnesses.

Wayne County Prosecutor Kym Worthy said any case sent to her office that uses facial recognition technology cannot move forward without other supporting evidence.

"This case should not have been issued based on the DPD investigation, and for that we apologize," Worthy said in a statement to NPR. "Thankfully, it was dismissed on our office's own motion. This does not in any way make up for the hours that Mr. Williams spent in jail."

Worthy said Williams is able to have the case expunged from his record.
Williams: "Let's say that this case wasn't retail fraud. What if it's rape or murder?"
According to Georgetown Law's Center on Privacy and Technology, at least a quarter of the nation's law enforcement agencies have access to face recognition tools.

"Most of the time, people who are arrested using face recognition are not told face recognition was used to arrest them," said Jameson Spivack, a researcher at the center.

While Amazon, Microsoft and IBM have announced a halt to sales of face recognition technology to law enforcement, Spivack said that will have little effect, since most major facial recognition software contracts with police are with smaller, more specialized companies, like South Carolina-based DataWorks Plus, which is the company that supplied the Detroit Police Department with its face-scanning software.

The company did not respond to an interview request.

DataWorks Plus has supplied the technology to government agencies in Santa Barbara, Calif., Chicago and Philadelphia.

Facial recognition technology is used by consumers every day to unlock their smartphones or to tag friends on social media. Some airports use the technology to scan passengers before they board flights.

Its deployment by governments, though, has drawn concern from privacy advocates and experts who study the machine learning tool and have highlighted its flaws.

"Some departments of motor vehicles will use facial recognition to detect license fraud, identity theft, but the most common use is law enforcement, whether it's state, local or federal law enforcement," Spivack said.

The government use of facial recognition technology has been banned in half a dozen cities.

In Michigan, Williams said he hopes his case is a wake-up call to lawmakers.

"Let's say that this case wasn't retail fraud. What if it's rape or murder? Would I have gotten out of jail on a personal bond, or would I have ever come home?" Williams said.

Williams and his wife, Melissa, worry about the long-term effects the arrest will have on their two young daughters.

"Seeing their dad get arrested, that was their first interaction with the police. So it's definitely going to shape how they perceive law enforcement," Melissa Williams said.

In his complaint, Williams and his lawyers say if the police department won't ban the technology outright, then at least his photo should be removed from the database, so this doesn't happen again.

"If someone wants to pull my name and look me up," Williams said, "who wants to be seen as a thief?"

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'The Computer Got It Wrong': How Facial Recognition Led To False Arrest Of Black Man

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Police in Detroit were trying to figure out who stole five watches from a Shinola retail store. Authorities say the thief took off with an estimated $3,800 worth of merchandise. Investigators pulled a security video that had recorded the incident.

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The controversy I initially wrote didn't have to do with algorithmic bias since it was the sentencing (by a regular judge) of al old man who killed the armed burgler that was in his house. I would say it's a bias inherent in society thinking killing someone is bad and not understanding the reasons behind that killing (deontological before teleological perspective)

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What's hard about systems that are created to forecast future acts/behaviors especially in items that deal with the justice system or crime is that they cannot factor in context.

Take a simple question like: has one of your parents been to jail in the past two years? with the answers being 'yes' or 'no.' The context that is missing is why did they go to jail, what for/what type of crime? The context of this answer will likely play a role in the future acts or behaviors of the person that is currently being sentenced but the system doesn't take this into account.

There is never going to be a perfect system and so people shouldn't lean heavily on these systems but use them as a single data point in a collection of data points they are using to help make decisions.

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Algorithmic bias in predictive policing, challenges/concerns in Canada

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Algorithmic bias in predictive policing, challenges/concerns in Canada

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The article's author highlights that in Canada, predictive policing uses historical crime data, which is biased, and algorithms based on this data reproduce these biases - this is where the controversy lies. This is a clear example of algorithmic bias, where the technology perpetuates existing prejudices embedded in the data it relies on. The author also points out that AI policies in Canada do not adequately regulate the use of these technologies by law enforcement and do not protect marginalized communities against potential police abuse. Closing these gaps is crucial for fair policing. This situation raises important questions about the broader role of algorithmic bias in perpetuating inequality, and specifically systemic bias, and the necessity of robust AI regulations to ensure justice and fairness.

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Historical crime data is inherently biased, and producing algorithms based on that data reproduces those very biases.

pixinoo / iStock / Getty Images Plus

In February 2019, Nijeer Parks was arrested in Woodbridge, New Jersey, based on a facial recognition match that linked him to several crimes. Parks faced charges of aggravated assault, unlawful possession of weapons, using a fake ID, possession of marijuana, shoplifting, leaving the scene of a crime and resisting arrest.

In November 2019, the case against him was dismissed - there was no evidence of him committing a crime besides a faulty facial recognition match.

In the near year between being arrested and being cleared of those crimes, Parks faced a legal and personal nightmare. Before even learning what the evidence against him was, he spent 11 days in jail, and over the next year, paid nearly $5,000 to defend himself against a crime he did not commit. On top of this, Parks has yet to receive an apology for any wrongdoings committed against him.

"I've never heard anything from anybody else.... No, 'We're sorry. We could have went about it a different way'," said Parks in an interview with CNN . "Nothing."

He is now in the midst of an ongoing lawsuit against New Jersey police and prosecutors.

Parks was the third known person in the United States to have been falsely arrested based on a faulty facial recognition match, joining Robert Williams and Michael Oliver - all Black men. While this has yet to happen in Canada, marginalized peoples in this country are in danger of facing similar issues if the federal government does not act provocatively.

Canadian Outlook

Canadian law enforcement agencies are ingrained with systemic biases, confirmed by the House of Commons in 2021 in their Report of the Standing Committee on Public Safety and National Security.

The use of algorithmic policing technologies, such as facial recognition, in police work has steadily increased over the past several years, as by The Citizen Lab and the University of Toronto's International Human Rights Program .

This has created the issue of predictive policing in Canada, which relies on historical crime data to forecast crime. Proponents of predictive policing argue that it predicts crime more effectively than traditional policing methods, eliminating bias. However, historical crime data is inherently biased, and producing algorithms based on that data reproduces those very biases.

What compounds this issue is that Canada's current artificial intelligence (AI) policies are severely lacking in regulating how law enforcement use algorithmic policing technologies, and in providing protections for marginalized peoples (Black, Indigenous, Asian, Brown, transgender, etc.) from potential police abuse.

Experts such as Canadian AI governance researcher Ana Brandusescu want the federal government to act proactively and ban these technologies outright. If not an outright ban, she believes that transparency and accountability are important principles that must be incorporated into Canada's AI policies and the procurement of AI technologies.

These principles would help provide "a really clear idea of how public money is spent, and where it's going," said Brandusescu in an interview with Truthout.

Types of Algorithmic Policing Technologies

Location-based algorithmic technology identifies "where and when potential criminal activity might occur" by using historical (often problematic) police data, as defined by a published by The Citizen Lab.

The Vancouver Police Department's use of GeoDASH is an example of this; the technology can disproportionately target the marginalized and vulnerable communities that live in Vancouver's Downtown Eastside.

Person-focused algorithmic technologies rely "on data analysis ... to identify people who are more likely to be involved in potential criminal activity," according to The Citizen Lab.

Another example is the Calgary Police Service's use of Gotham, a data analysis software developed by the defense company .

Gotham provides the Police Service with "physical characteristics, relationships, interactions with police, religious affiliation, and possible involved activities," while also "[mapping] out the location of purported crime and calls for services," wrote The Citizen Lab.

While surveillance technologies do not have a predictive element, they have their own set of issues - as the falsely arrested Parks, Williams and Oliver can attest to.

Tested and proven by prominent AI scholars such as Joy Buolamwini and Timnit Gebru, facial recognition technology fails to accurately register the faces of racially marginalized and trans people. The facial data that facial recognition technology is trained on is largely of white, gender-normative faces.

Current Landscape

At the moment, there are two policies in Canada that concern AI: the Directive on Automated Decision Making (ADM) and the Algorithmic Impact Assessment (AIA), both developed by the Treasury Board Secretariat.

According to AI governance researcher Brandusescu, "the Directive on ADM or AIA are not doing enough to support public accountability."

The AIA is a mandatory risk assessment questionnaire for companies with 81 questions about their business process, algorithm(s), data and how they designed their systems. However, because of the lack of independent oversight, there is no measure in place to prevent companies from treating it as a rubber-stamp exercise.

The Directive on ADM is only applicable to AI technologies developed in-house by the federal government or outsourced to private companies.

However, it has no power over AI technologies developed for provincial use, and no power over private companies who develop their technology on their own accord and then either sell it to different governmental institutions or offer free trials, which is what happened with Clearview AI.

Prior to being published, an internal draft review of the Directive on ADM by the Treasury Board Secretariat raised concerns regarding the legal and ethical use of AI in policing as "algorithms are trained on historical data, [and] their users run the risk of perpetuating past injustices and discriminatory policing practices."

In an interview with Truthout, Sean Benmor, a senior communication advisor for the Department of Innovation, Science, and Economic Development (ISED) Canada, responding on behalf of the Treasury Board Secretariat said, "The use of algorithmic technologies in law enforcement could be subject to the Directive [on ADM] if they are in scope."

However, what is outlined in the scope of the Directive on ADM is vague. According to section 5.2, it is applicable "to any system, tool, or statistical models used to recommend or make an administrative decision about a client."

It is unclear what an administrative decision about a "client" means. Does it mean the use of an algorithmic technology to make an arrest? Does the Vancouver Police Department's use of GeoDASH and the Calgary Police Service's use of Palantir Gotham fall under section 5.2?

In addition, not all algorithmic policing technologies are automated decision-making systems, meaning there is another gaping hole in the policy. The draft review pointed out that the Directive on ADM would not have covered Clearview AI's facial recognition technology "because the tool itself did not make any decisions."

If there is no current policy dedicated to regulating facial recognition technology - the tool that produced the faulty matches that led to the arrests of Parks, Miller and Oliver - then that is a serious issue that the federal government must address.

According to Treasury Board Secretariat representatives who spoke at the first public gathering on the Directive on ADM in November 2021, the final version of the internal review was supposed to be published by early 2022, but it has not been finished yet.

Benmor told Truthout, "Work is underway for a regular review of the Directive, which includes consideration of additional measures to strengthen the instrument's approach to addressing bias."

The Digital Charter Implementation Act

Bill C-11, the Digital Charter Implementation Act, was a proposed policy that sook to strengthen digital privacy protections for people in Canada. It died on the order paper of the 2021 federal election after receiving only two readings, but its contents represent the state of digital privacy reform on a federal level.

It was created in light of Clearview AI's privacy violations that caused the federal government to reexamine its existing frameworks. Forty-eight Canadian law enforcement agencies - including the Royal Canadian Mounted Police - admitted to using the U.S. tech company's facial recognition technology, database of images and biometric facial arrays of people in Canada.

This was an inflection point for Canada's public and political discourse concerning facial recognition.

When asked about Bill C-11's future, Benmor said, "Minister [François-Philippe Champagne] has indicated that new legislation will consider stakeholder's comments on the former Bill C-11.... One such comment pertained to the need for greater transparency and accountability on the part of organizations who are developing and using AI systems which may impact Canadians."

Solutions

On March 21, AI governance researcher Brandusescu provided testimony as an expert on the use of and impact of facial recognition for Canada's House of Commons's Standing Committee on Access to Information, Privacy, and Ethics . She proposed several solutions that can be applied to algorithmic technologies in general.

Clearview AI was able to work around Canada's existing digital privacy frameworks because they provided law enforcement with their technology on a trial basis, meaning there was no contract involved and there was no measure in place to regulate that.

In her testimony, Brandusescu recommended that the Office of the Privacy Commissioner "create a policy for the proactive disclosure of free software trials used by law enforcement, and all of government, as well as create a public registry for them."

She also maintains that a public registry is necessary for all AI technologies, especially those used by law enforcement. "A public AI registry will be useful for researchers, academics, and investigative journalists to inform the public," she said.

For companies linked to human rights abuses, such as Palantir, she believes they should be removed from Canada's pre-qualified AI supplier list.

Regarding the existing AIA, "The Office of the Privacy Commissioner should work with the Treasury Board Secretariat to develop more specific, ongoing monitoring and reporting requirements so the public knows of the use of impact of a system has changed since the initial [assessment]," said Brandusescu.

Going Forward

As surveillance has become a fact of life, digital privacy must become a human right.

"While AI has the power to solve immense problems and enable unprecedented innovation, it can also create new challenges when left unchecked," Benmor said.

This acknowledgment is important but only if solutions are implemented alongside it as soon as possible. Brandusescu's recommendations will go a long way in preventing the injustices that happened to Parks, Williams and Oliver in the U.S. from happening to marginalized peoples in Canada.

Copyright © Truthout. May not be reprinted without permission.

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Canada's Predictive Policing Tech Is Poorly Regulated Under AI Policy

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As surveillance has become a fact of life, digital privacy must become a human right.

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Lack of Transparency over Police Forces’ Covert Use of Predictive Policing Software

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Lack of Transparency over Police Forces’ Covert Use of Predictive Policing Software

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This story shows elements of algorithmic biases mentioned by Barocas and Selbst including target label definition, data collection, proxies and possibly masking.

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Free from fear or favour

No tracking. No cookies

UK police forces are under scrutiny for their lack of transparency regarding the use of harmful technologies known to exacerbate racist policing

Lack of Transparency over Police Forces' Covert Use of Predictive Policing Software Raises Concerns about Human Rights Abuses

UK police forces are under scrutiny for their lack of transparency regarding the use of harmful technologies known to exacerbate racist policing

Police in the UK are routinely refusing to allow the public insight into the methods used in policing, citing exemptions on the grounds of national security and law enforcement for companies implicated in scandals or potential breaches of human rights.

Research conducted by The Citizens found that Freedom of Information (FOI) requests regarding multiple tech firms were met with almost identical responses by police forces, which could 'neither confirm nor deny' their use of covert software to monitor citizens' behaviour.

They cited the threat from terrorism and the potential in revealing the software used to "cause operational harm". However, there is plenty of documented evidence that these tools are often discriminatory and impact citizens' right to protest.

Currently, through the use of blanket exemption clauses - and without any clear legislative oversight - public access to information on systems that may be being used to surveil them remains opaque.

Companies including Palantir, NSO Group, QuaDream, Dark Matter and Gamma Group are all exempt from disclosure under the precedent set by the police, along with another entity, Dataminr.

Dataminr is a social media monitoring tool used in newsrooms, corporate settings and by police, with The Citizens revealing that, since 2017, the UK Government has spent more than £5.1 million on its technology. This has helped police in the US monitor and break up Black Lives Matter and Muslim rights activism through social media monitoring. Dataminr software has also been used by the Ministry of Defence, Foreign Commonwealth and Development Office, and the Cabinet Office, with the latest contract being revealed in February.

FOI requests for more information as to the nature of Dataminr's contracts with the Cabinet Office have been refused and appeals to the Information Commissioner's Office not upheld.

Aside from Dataminr's known government contracts, the company has links to public officials and has been spoken about favourably by government departments. The firm's success in monitoring public posts referencing the Coronavirus, for instance, has been noted by the Centre for Data Ethics and Innovation and at least three officials and parliamentarians have had dealings with it.

Dominic Fortescue, former government chief security officer, held an advisory position with the company between May and June 2022; while in 2020 former Metropolitan Police Commissioner Lord Bernard Hogan-Howe was paid to deliver an online video conference for the company in relation to its work for the Ministry of Defence.

Lord Nigel Darroch also held a consultancy position with the firm as late as 2022, with Dataminr featured among others at the 'future policing ' zone of last year's emergency services event, chaired by Lord Hogan-Howe.

Fortescue, Lord Hogan-Howe and Lord Darroch are not accused of racism or of condoning racist policing.

Monitoring social media to proactively collect data about future protests is a form of predictive policing. In 2016, the American Civil Liberties Union (ACLU) reported that US law enforcement was deploying the tool to surveil and prevent BLM protestors from gathering.

Dataminr has since been embroiled in scandals that have resulted in limitations of its use abroad. New research shows that, far from being a 'neutral' observational tool, Dataminr produces results that reflect its clients' politics, business goals and ways of operating.

Cambridge University's Dr Eleanor Drage and Dr Federica Frabetti of Roehampton University argue that protests that politically oppose the government are the ones that protest recognition tech is most likely to flag as potential threats - because it only shows clients (in this case law enforcement) protests that it is already concerned with.

In their analysis of a Dataminr patent, they have shown that the technology identifies 'dangerous' protests that align with clients' own worldview. Dataminr's surveillance mechanisms are therefore not general but proactively targeted at the protests that clients are preoccupied with. As the academics explain, if a Dataminr client is worried about BLM agitation, the system is far more likely to show up early signs of BLM mobilisations as 'dangerous' protests about to happen.

Following the murder of George Floyd in May 2020, Dataminr partnered with New York and Minneapolis police departments, alerting them to BLM protests by monitoring social media data and 'tracking' this data while clients "listened". These departments requested specific information about BLM protests and Dataminr software gathered pictures and status updates that protestors and bystanders were posting on social media platforms, packaging this information into alerts sent directly to law enforcement.

In 2016, hashtags and keywords such as #muslimlivesmatter and #BLM became 'trigger' words for US police. Dataminr combined these social media tracking tools with technologies such as natural language processing, audio processing and machine vision - which help identify who or what is in an image, including text extraction from photos. This combination of tools, it claims, gives a robust impression of how likely it is that a crowd of people will become a dangerous protest.

According to a Dataminr promotional blog, its software can identify a fire from social media posts and other sources. When the software flags images that contain elements that denote fire (such as smoke) in combination with the presence of hashtags and keywords like "fire" and "burning", the result is cross-checked and can be sent out as an alert.

While this process is relatively apolitical when applied to fires, teaching the software to associate certain kinds of images, text and hashtags with a 'dangerous' protest results in politically and racially-biased definitions of what dangerous protests look like. This is because, to make these predictions, the system has to decide whether the event resembles other previous events that were labelled 'dangerous' - for example, past BLM protests.

Because Dataminr has been extensively used to crackdown on BLM and other racially-informed protests, the system has been trained to make a connection between crowds of predominantly black and brown people and a potentially violent protest. Events labelled dangerous in the future need to correspond to events labelled as dangerous in the past to be validated as a correct prediction. As Frabetti and Drage have observed, Dataminr is therefore disproportionately likely to flag gatherings with comparable demographics as dangerous. And yet, Dataminr calls this a "prediction" - suggestive of Minority Report-style policing whereby it is possible to see and prevent crime before it happens.

Events surrounding Dataminr's partial ban in the US have shown the difficulties of limiting law enforcement's use of these technologies.

When in 2016 the ACLU proved that Dataminr's interventions were contributing to racist policing, the company was subsequently banned from granting fusion centres in the US direct access to Twitter's API. Fusion centres are state-owned and operated facilities and serve as focal points to gather, analyse and redistribute intelligence among state, local, tribal and territorial (SLTT), federal and private sector partners to detect criminal and terrorist activity.

However, US law enforcement found a way around these limitations by continuing to receive Dataminr alerts outside of fusion centres.

There are currently no access limitations to how law enforcement and border control-related clients in other countries access Dataminr's full services. In 2016 Dataminr worked with South African law enforcement, for instance, to monitor students at the #Shackville protests, which aimed to combat black students' lack of access to decent university accommodation at the University of Cape Town.

Drage and Frabetti's research also shows that this software is more likely to flag 'left-wing' protests and racial minority protestors as potentially dangerous, having been trained on BLM and the Shackville protests.

Since Dataminr is based on machine learning technologies, it 'learns' to identify a protest by scanning large quantities of data, making associations based on past events "according to law enforcement's existing concerns and perceptions of what a protest is and what kind of people are likely to make it turn dangerous". If it is trained on police data, Dataminr can therefore learn to associate gatherings of black people or BLM slogans with violent protests.

This process does not end with training. It continues while the technology is deployed by law enforcement, because, as Drage and Frabetti explain, "if the client feeds back to the system that certain alerts are or are not working for them, this in turn reinforces or adjusts the knowledge base and the way the system generates classification rules".

Therefore the software reflects patterns of discrimination and further embeds them into police practice. Discriminatory algorithms that predict crime based on data about the past (for example, where crime is likely to be committed or how an individual or a group will behave) lead to the over-monitoring of certain areas, groups or individuals. They can also lead to life-changing decisions that, due to the lack of transparency and understanding of how these algorithms work, are extremely difficult to challenge.

The lack of transparency and oversight in how these technologies are used is deeply concerning, especially in the wake of the UK Government's attempts via the Public Order Bill at curtailing protests such as Just Stop Oil's 'slow marching' before disruption begins. The bill has been contested in the House of Lords and by campaigners, with Labour peer Shami Chakrabarti condemning it because "these powers are going to be used and abused by accident or design against people who may not even be protestors".

A 2021 inquiry by Parliament's Justice and Home Affairs committee was "taken aback by the proliferation of artificial intelligence tools potentially being used without proper oversight, particularly by police forces". The report found that use of algorithmic tools in crime prevention was "a new Wild West" and that "all 43 police forces are free to individually commission whatever tools they like" as part of an 'opaque' system in need of overhaul.

The Government rejected most of the report's findings - notably the creation of an oversight body and a certification system for new technologies, stating that it was "not persuaded by the arguments put forward". This was despite the report finding that these softwares could "broadly undermine human rights".

Following the report's publication, then Crime and Policing Minister Kit Malthouse said that legislating the police's deployment of advanced algorithmic tools might "stifle innovation" and that the use of new technologies could be settled in court.

Griff Ferris, legal officer at criminal justice watchdog Fair Trials, said in response that "the Government should legislate to provide proper legal safeguards in their use, or in the case of certain technologies, prohibit their use entirely, and not rely on people challenging them in court".

"Taking the government or police to court should be a last resort, not the sole way of holding them accountable for their actions, not least when litigation is extremely expensive and the majority of people will not be able to afford it," he added.

While the threat from terrorism is very real, there remains a serious question of oversight in how advanced technologies are implemented, made worse by the lack of transparency. In addition to facial recognition, police have also previously been taken to court by campaign groups for refusing to confirm or deny its use of other covert tactics, such as international mobile subscriber identity software (IMSIs), used to intercept mobile data.

At least 14 police forces in the UK are also known to have used or considered predictive policing technologies, with campaigners arguing that "we cannot be sure that these programmes have been developed free of bias and that they will not disproportionately adversely impact on certain communities or demographics". Use of these technologies have, in the past, not been subject to public consultation and, without basic scrutiny at either a public or legislative level, there remains no solid mechanism for independent oversight of their use by law enforcement.

Dataminr was approached for comment.
Additional reporting by Dr Eleanor Drage and Dr Federica Frabetti

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Lack of Transparency over Police Forces' Covert Use of Predictive Policing Software Raises Concerns about Human Rights Abuses

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UK police forces are under scrutiny for their lack of transparency regarding the use of harmful technologies known to exacerbate racist policing

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The controversy at Edison Senior High School in Miami in 2008 appears to be an example of disparate impact, where a seemingly neutral policy or practice disproportionately affects a protected group, rather than algorithmic bias. The issue was likely rooted in broader systemic biases and inequities, not a specific algorithm.

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Predictive policing algorithms are racist. They need to be dismantled.

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Lack of transparency and biased training data mean these tools are not fit for purpose. If we can’t fix them, we should ditch them.

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Bias is created by the echo chamber of what you repetitively research, read, and watch regardless of the authenticity and reality, disallowing people to learn the world from different perspectives. And that is what unhealthy, sided media brought us.

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Bias is created by the echo chamber of what you repetitively research, read, and watch regardless of the authenticity and reality, disallowing people to learn the world from different perspectives. And that is what unhealthy, sided media brought us.

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Humans are primarily responsible for the creation of bias. Before any system can analyze and predict outcomes, it relied on a human's initial data that was either skewed, prejudicial or straight discriminatory.

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I agree with the notion that we cannot completely remove or account for every bias because we, as humans, inherently have bias through having individual perspectives or regional cultures when grouped. Though that may be the case I don’t believe that’s the issue but rather mitigating any potential harm from the necessary minimum bias that our datasets might have through the human gained bias whether that be in the data itself or how the data is labeled and targeted.

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Algorithms will always be biased, becase everything in our society has a cultural context, so if a program asks if your parents have been to jail, it isn´t a necessarily racist question, the problem is that the system will not consider the context if this person, or group of person lifes.

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The problem that we are controlling the selecting based on nationality , also targeting people who have certain  budget and looking for certain item which is house in this case

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Chinese effort to gather ‘micro clues’ on Uyghurs

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Chinese effort to gather ‘micro clues’ on Uyghurs

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Chinese Communist party’s grip on the largely Muslim and purportedly autonomous region, going beyond police crackdowns and mass arrests to ensure total control.