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Naisha's  groupThe Impact of Algorithm Biases on Society Justifies Stricter Regulations on Technology Companies

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1. Being acutely aware of those risks and working to reduce them is an urgent priority

2. Stay up to-date on this fast-moving field of research

3. Establish responsible processes

4. Portfolio of technical tools, as well as operational practices such as internal “red teams,” or third-party audits.

5. Fact-based conversations

6. “explainability techniques”

7. "human-in-the-loop” processes.

8. invest more, provide more data, and take a multi-disciplinary approach in bias research

9. invest more in diversifying the AI field itself.

10. trust

11. Bias is all of our responsibility.

12. improve on human decision-making

13. responsibility to encourage progress on research and standards that will reduce bias in AI.

14. we must responsibly take advantage of the several ways that AI can improve on traditional human decision-making.

15. understanding and measuring “fairness.

16. “counterfactual fairness,”

17. if people trust these systems to produce unbiased results. AI can help humans with bias — but only if humans are working together to tackle bias in AI.

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Over the past few years, society has started to wrestle with just how much human biases can make their way into artificial intelligence systems-with harmful results. At a time when many companies are looking to deploy AI systems across their operations, being acutely aware of those risks and working to reduce them is an urgent priority. What can CEOs and their top management teams do to lead the way on bias and fairness? Among others, we see six essential steps: First, business leaders will need to stay up to-date on this fast-moving field of research. Second, when your business or organization is deploying AI, establish responsible processes that can mitigate bias. Consider using a portfolio of technical tools, as well as operational practices such as internal "red teams," or third-party audits. Third, engage in fact-based conversations around potential human biases. This could take the form of running algorithms alongside human decision makers, comparing results, and using "explainability techniques" that help pinpoint what led the model to reach a decision - in order to understand why there may be differences. Fourth, consider how humans and machines can work together to mitigate bias, including with "human-in-the-loop" processes. Fifth, invest more, provide more data, and take a multi-disciplinary approach in bias research (while respecting privacy) to continue advancing this field. Finally, invest more in diversifying the AI field itself. A more diverse AI community would be better equipped to anticipate, review, and spot bias and engage communities affected.

Human biases are well-documented, from implicit association tests that demonstrate biases we may not even be aware of, to field experiments that demonstrate how much these biases can affect outcomes. Over the past few years, society has started to wrestle with just how much these human biases can make their way into artificial intelligence systems - with harmful results. At a time when many companies are looking to deploy AI systems across their operations, being acutely aware of those risks and working to reduce them is an urgent priority.

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Over the past few years, society has started to wrestle with just how much human biases can make their way into artificial intelligence systems-with harmful results. At a time when many companies are looking to deploy AI systems across their operations, being acutely aware of those risks and working to reduce them is an urgent priority.

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My opinion is that algorithm biases have long term effects on people and the society, as they take on characteristics of their programmers, thus segregating the society in different ways.

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My opinion is that algorithm biases silently wreak havoc by reflecting human prejudices that have an adverse impact on our lives

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My opinion is that algorithm biases silently wreak havoc by reflecting human prejudices that have an adverse impact on our lives

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My opinion is that algorithm biases silently wreak havoc by reflecting human prejudices that have an adverse impact on our lives

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My opinion is that algorithm biases silently wreak havoc by reflecting human prejudices which have an adverse impact on our lives./

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Article 1: My opinion is that in the labyrinth of everyday decisions, biases act as unseen puppeteers, shaping our choices like a masterful illusionist.

Article 2: My opinion is that Algorithm bias is like a blurry pair of spectacles that demands transparency as the cleansing solution.

Video 1: My opinion is that Algorithms, often labelled as objective, are the subjective opinions of coded biases, shaping our fate with just 1 line of code.

Article 3: My opinion is that Algorithm bias is like a Trojan Horse infiltrating justice, like a virus infecting our body.

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My opinion is that algorithmic neutrality is a myth and algorithms have deeply destructive effects.

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Aratrika's group: Can AI be free of bias

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Image 1 - This image makes me think about the impact of artificial intelligence on humans. It shows how AI sorts people into different groups and classifies them based on its training. For example, in an article Ben Dickson tells us how 'black people' were recognised as gorillas as AI had not been given enough images. This is problematic as, if not given enough information it can lead to unfair judging.

Image 2 - In this image I can see an AI robot sweeping away data. It shows us the main cause of how artificial intelligence is biased - lack of data. AI highlights human biases, as if the bias had been absent in the first place, enough information would have been given, resulting in equal and fair results. For example, in a job interview, if not given enough data AI will appoint people with higher social status at the better positions.

Image 3 - These headlines make me think of the biases in our world. They bring out the biases of people, as they tell us that, we all know about racism and yet no one takes actions to stop it. It shows how AI takes the qualities of humans and the impact it causes, leading us to removing biased AI.

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The headlines in the image show how algorithm biases also discriminate against those who are given less value.

I.E- women and other minority races

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These headlines make me think how AI can lead to promoting pre-existing human biases. Gender bias, racism, and many more biases get furthered by Algorithm biases. For example, how Google Images classified pictures of blacks as 'gorrilas'. This simply cannot be waived off as a blind spot - rather, reveals how AI inherits the biases of its creators - us, humans.

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This image shows how human biases reflect on AI's divisions. As we can see, AI is classifying people into groups on the surface level, such as race and gender, in the same manner humans do. We can see different sects being formed, and the distance between groups shows the image difference according to the algorithm

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In this image, we see an AI robot with a broom - sweeping away all the data together in the same way. This makes me think how AI can blindly group people and "sweep away" people by thinking they are in the same category without actually assessing the data. This can have severe consequences as AI might favour men's profiles for tech jobs rather than women. It might directly trash all women applications without assessing each one due to its training data and bias. As AI is not human, it would simply take it for granted "tech jobs belong to men."

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Image 1:

This image makes me think how Artificial intelligence is controlling and dominating humans. It is problematic because AI cannot understand the complexities of human values and the utility function could allow potentially harmful scenarios. It is beneficial because it can significantly reduce errors and increase accuracy and precision however if AI gets too much control and power it can be a threat to humans.

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This image makes me think how AI sorts people into groups based on its training data and biases. This is problematic because AI would blindly sort people without actually correctly assessing them and this could lead to severe repercussions. For example, in some areas, judges rely heavily on AI based programs to sort which cases to take up and which to directly send to jail. A ProPublica Investigation on this found that AI tended to be biased towards black people by assessing them at a higher risk of committing a crime than white people. This might even lead to people going to jail. This is why AI biases are so problematic.

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Words - Context Algorithm Biases - repeatedly used in the article  that are explained in the article 

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Words - Context Algorithm Biases - repeatedly used in the article  that are explained in the article 

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Data

Algorithimic neutrality

Conversational interfaces

Deep learning

Machine learning

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Joy Buolamwini: How I'm fighting bias in algorithms

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MIT grad student Joy Buolamwini was working with facial analysis software when she noticed a problem: the software didn't detect her face -- because the people who coded the algorithm hadn't taught it to identify a broad range of skin tones and facial structures.

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Yes, “algorithms” can be biased. Here’s why

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Op-ed: a computer scientist weighs in on the downsides of AI.

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Cathy O'Neil: The era of blind faith in big data must end

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Algorithms decide who gets a loan, who gets a job interview, who gets insurance and much more -- but they don't automatically make things fair. Mathematician and data scientist Cathy O'Neil coined a term for algorithms that are secret, important and harmful: "weapons of math destruction."

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Though the concept of illusory superiority arguably dates back to Confucius and Socrates, it may come as a shock that its discussion in the form of the Dunning-Kruger Effect is almost 20 years old; and though it may simply be a result of an echo chamber created through my own social media, it seems to be popping up quite frequently in the news and posts that I've been reading lately - even through memes! For those of you unfamiliar with the phenomenon, the Dunning-Kruger Effect refers to a cognitive bias in which individuals with a low level of knowledge in a particular subject mistakenly assess their knowledge or ability as greater than it is. Similarly, it also refers to experts underestimating their own level of knowledge or ability.

But, then again, maybe it's not my echo chamber... maybe it is part and parcel of our new knowledge economy (Dwyer, 2017; Dwyer, Hogan & Stewart, 2014) and the manner in which we quickly and effortlessly process information (right or wrong) with the help of the internet. In any case, given the frequency with which I seem to have encountered mention of this cognitive bias lately, coupled with the interest in my previous blog post 18 Common Logical Fallacies and Persuasion Techniques, I decided it might be interesting to compile a similar list - this time, one of cognitive biases.

A cognitive bias refers to a 'systematic error' in the thinking process. Such biases are often connected to a heuristic, which is essentially a mental shortcut - heuristics allow one to make an inference without extensive deliberation and/or reflective judgment, given that they are essentially schemas for such solutions (West, Toplak, & Stanovich, 2008). Though there are many interesting heuristics out there, the following list deals exclusively with cognitive biases. Furthermore, these are not the only cognitive biases out there (e. g. there's also the halo effect and the just world phenomenon); rather, they are 12 common biases that affect how we make everyday decisions, from my experience.
1. The Dunning-Kruger Effect
In addition to the explanation of this effect above, experts are often aware of what they don't know and (hopefully) engage their intellectual honesty and humility in this fashion. In this sense, the more you know, the less confident you're likely to be - not out of lacking knowledge, but due to caution. On the other hand, if you know only a little about something, you see it simplistically - biasing you to believe that the concept is easier to comprehend than it may actually be.
2. Confirmation Bias
Just because I put the Dunning-Kruger Effect in the number one spot does not mean I consider it the most commonly engaged bias - it is an interesting effect, sure; but in my Critical Thinking classes, the Confirmation Bias is the one I constantly warn students about. We all favour ideas that confirm our existing beliefs and what we think we know. Likewise, when we conduct research, we all suffer from trying to find sources that justify what we believe about the subject. This bias brings to light the importance of, as I discussed in my previous post on 5 Tips for Critical Thinking, playing Devil's Advocate. That is, we must overcome confirmation bias and consider both sides (or, if there are more than two, all sides) of the story. Remember, we are cognitively lazy - we don't like changing our knowledge (schema) structures and how we think about things.
3. Self-Serving Bias
Ever fail an exam because your teacher hates you? Ever go in the following week and ace the next one because you studied extra hard despite that teacher? Congratulations, you've engaged the self-serving bias! We attribute successes and positive outcomes to our doing, basking in our own glory when things go right; but, when we face failure and negative outcomes, we tend to attribute these events to other people or contextual factors outside ourselves.
4. The Curse of Knowledge and Hindsight Bias
Similar in ways to the Availability Heuristic (Tversky & Kahneman, 1974) and to some extent, The False Consensus Effect, once you (truly) understand a new piece of information, that piece of information is now available to you and often becomes seemingly obvious. It might be easy to forget that there was ever a time you didn't know this information and so, you assume that others, like yourself, also know this information: the Curse of Knowledge. However, it is often an unfair assumption that others share the same knowledge. The Hindsight Bias is similar to the Curse of Knowledge in that once we have information about an event, it then seems obvious that it was going to happen all along. I should have seen it coming!
5. Optimism/Pessimism Bias
As you probably guessed from the name, we have a tendency to overestimate the likelihood of positive outcomes, particularly if we are in good humour, and to overestimate the likelihood of negative outcomes if we are feeling down or have a pessimistic attitude. In either the case of optimism or pessimism, be aware that emotions can make thinking irrational. Remember, one of my 5 Tips for Critical Thinking, Leave emotion at the door!
6. The Sunk Cost Fallacy
Though labelled a fallacy, I see 'Sunk Cost' just as much in tune with bias as faulty thinking, given the manner in which we think in terms of winning, losing and 'breaking even'. For example, we generally believe that when we put something in, we should get something out - whether it's effort, time or money. With that, sometimes we lose... and that's it - we get nothing in return. A sunk cost refers to something lost that cannot be recovered. Our aversion to losing (Kahneman, 2011) makes us irrationally cling to the idea of 'regaining', even though it has already been lost (known in gambling as chasing the pot - when we make a bet and chase after it, perhaps making another bet to recoup the original [and hopefully more] even though, rationally, we should consider the initial bet as out-and-out lost). The appropriate advice of cutting your losses is applicable here.
7. Negativity Bias
Negativity Bias is not totally separate from Pessimism Bias, but it is subtly and importantly distinct. In fact, it works according to similar mechanics as the Sunk Cost Fallacy in that it reflects our profound aversion to losing. We like to win, but we hate to lose even more. So, when we make a decision, we generally think in terms of outcomes - either positive or negative. The bias comes into play when we irrationally weigh the potential for a negative outcome as more important than that of the positive outcome.
8. The Decline Bias (a.k.a. Declinism)
You may have heard the complaint that the internet will be the downfall of information dissemination; but, Socrates reportedly said the same thing about the written word. Declinism refers to bias in favour of the past over and above 'how things are going'. Similarly, you might know a member of an older generation who prefaces grievances with 'Well, back in my day' before following up with how things are supposedly getting worse. The Decline Bias may result from something I've mentioned repeatedly in my posts - we don't like change. People like their worlds to make sense, they like things wrapped up in nice, neat little packages. Our world is easier to engage when things make sense to us. When things change, so must the way in which we think about them; and because we are cognitively lazy (Kahenman, 2011; Simon, 1957), we try our best to avoid changing our thought processes.
9. The Backfire Effect
The Backfire Effect refers to the strengthening of a belief even after it has been challenged. Cook and Lewandowsky (2011) explain it very well in the context of changing people's minds in their Debunking Handbook. The Backfire Effect may work based on the same foundation as Declinism, in that we do not like change. It is also similar to Negativity Bias, in that we wish to avoid losing and other negative outcomes - in this case, one's idea is being challenged or rejected (i.e. perceived as being made out to be 'wrong') and thus, they may hold on tighter to the idea than they had before. However, there are caveats to the Backfire Effect - for example, we also tend to abandon a belief if there's enough evidence against it with regard to specific facts.
10. The Fundamental Attribution Error
The Fundamental Attribution Error is similar to the Self-Serving Bias, in that we look for contextual excuses for our failures, but generally blame other people or their characteristics for their failures. It also may stem from the Availability Heuristic in that we make judgments based only on the information we have available at hand. One of the best textbook examples of this integrates stereotyping: Imagine you are driving behind another car. The other driver is swerving a bit and unpredictably starts speeding up and slowing down. You decide to overtake them (so as to no longer be stuck behind such a dangerous driver) and as you look over, you see a female behind the wheel. The Fundamental Attribution Error kicks in when you make the judgment that their driving is poor because they're a woman (also tying on to an unfounded stereotype). But what you probably don't know is that the other driver has three children yelling and goofing around in the backseat, while she's trying to get one to soccer, one to dance and the other to a piano lesson. She's had a particularly tough day and now she's running late with all of the kids because she couldn't leave work at the normal time. If we were that driver, we'd judge ourselves as driving poorly because of these reasons, not because of who we are. Tangentially, my wife is a much better driver than I.
11. In-Group Bias
As we have seen through consideration of the Self-Serving Bias and the Fundamental Attribution Error, we have a tendency to be relatively kind when making judgments about ourselves. Simply, In-Group Bias refers to the unfair favouring of someone from one's own group. You might think that you're unbiased, impartial and fair, but we all succumb to this bias, having evolved to be this way. That is, from an evolutionary perspective, this bias can be considered an advantage - favouring and protecting those similar to you, particularly with respect to kinship and the promotion of one's own line.
12. The Forer Effect (a.k.a. The Barnum Effect)
As in the case of Declinism, to better understand the Forer Effect (commonly known as the Barnum Effect), it's helpful to acknowledge that people like their world to make sense. If it didn't, we would have no pre-existing routine to fall back on and we'd have to think harder to contextualise new information. With that, if there are gaps in our thinking of how we understand things, we will try to fill those gaps in with what we intuitively think makes sense, subsequently reinforcing our existing schema(s). As our minds make such connections to consolidate our own personal understanding of the world, it is easy to see how people can tend to process vague information and interpret it in a manner that makes it seem personal and specific to them. Given our egocentric nature (along with our desire for nice, neat little packages and patterns), when we process vague information, we hold on to what we deem meaningful to us and discard what is not. Simply, we better process information we think is specifically tailored to us, regardless of ambiguity. Specifically, the Forer Effect refers to the tendency for people to accept vague and general personality descriptions as uniquely applicable to themselves without realizing that the same description could be applied to just about everyone else (Forer, 1949). For example, when people read their horoscope, even vague, general information can seem like it's advising something relevant and specific to them.

While heuristics are generally useful for making inferences, through providing us with cognitive shortcuts that help us stave off decision fatigue, some forms of heuristics can make our judgments irrational. Though various cognitive biases were covered in this post, these are by no means the only biases out there - just the most commonly engaged, in my experience, with respect to everyday decision-making. If you're interested in learning more about these and other cognitive biases, I recommend checking out yourbias.is. Remember, we make thousands of decisions every day, some more important than others. Make sure that the ones that do matter are not made based on bias, but rather on reflective judgment and critical thinking!

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12 Common Biases That Affect How We Make Everyday Decisions

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Though the concept of illusory superiority arguably dates back to Confucius and Socrates, it may come as a shock that its discussion in the form of the Dunning-Kruger Effect is almost 20 years old; and though it may simply be a result of an echo chamber created through my own social media, it seems to be popping up quite frequently in the news and posts that I've been reading lately - even through memes!

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This article is part of Demystifying AI, a series of posts that (try) to disambiguate the jargon and myths surrounding AI.
In early 2016, Microsoft launched Tay, an AI chatbot that was supposed to mimic the behavior of a curious teenage girl and engage in smart discussions with Twitter users. The project would display the promises and potential of AI-powered conversational interfaces.

However, in less than 24 hours, the innocent Tay became a racist, misogynist and a holocaust denying AI, debunking-once again-the myth of algorithmic neutrality. For years, we've thought that artificial intelligence doesn't suffer from the prejudices and biases of its human creators because it's driven by pure, hard, mathematical logic.

However, as Tay and several other stories have shown, AI might manifest the same biases as humans, and in some cases, it might even be worse. The phenomenon, known as "algorithmic bias," is rooted in the way AI algorithms work and is becoming more problematic as software becomes more and more prominent in every decision we make.

RELATED: What is the difference between narrow, general and super artificial intelligence?

The roots of algorithmic bias

Machine learning and deep learning, the most popular branches of AI, are the reason our software become biased. Deep learning algorithms are dependent on data, lots of it. Give an image classification algorithm millions of labeled cat pictures and it will be able to tell you whether a photo it hasn't seen before contains a cat. Give a speech recognition algorithm millions of voice samples along with their corresponding written words, and it will be able to transcribe spoken language faster than most humans.

The more labeled data an algorithm sees, the better it becomes at the task it performs. However, the tradeoff to this approach is that deep learning algorithms will develop blind spots based on what is missing or is too abundant in the data they're trained on.

For instance, in 2015, Google's photos app mistakenly tagged a photo of two black people as gorillas because its algorithm hadn't been trained with enough images of dark-skinned persons. In another case, the AI judge of a beauty contest mostly chose white participants as winners because its training was done on images of white people.

These are trivial cases that can be easily remedied by providing the AI with more samples in areas where it doesn't have enough data. In other cases where AI is working with vast amounts existing data in the endless sea of online information, finding and countering bias becomes much more difficult.

An example is a joint project by researchers at Microsoft and Boston University, in which they found sexist biases in word embedding algorithms, which are used in search engines, translation and other software that depend on natural language processing. Among their findings about the behavior of word embedding algorithms was a tendency to associate words such as "programming" and "engineering" to men and "homemaker" to women. In this case, the bias was ingrained in the thousands of articles the algorithms had automatically scavenged and analyzed form online sources such as Google News and Wikipedia. For instance, the tech industry is mostly dominated by men. This means that you're more likely to see male names and pronouns appear next to engineering and executive tech jobs. As humans, we acknowledge this as a social problem that we need to address. But a mindless algorithm analyzing the data would conclude that tech jobs should belong to men and wouldn't see it as a lack of diversity in the industry.

In the case of Tay, the Twitter users who interacted with the chatbot were more interested in teaching it hateful speech than engaging in meaningful conversations. Again, the AI was not to blame. The culprit was the general culture that Twitter as a social media breeds.

Why is algorithmic bias a serious problem?

Algorithmic bias is not new. Academics and experts have been warning about it for years. However, what makes it especially critical at this time is the prominence algorithms are finding in everyday decisions we make.

Take the word embedding algorithm problem we visited in the previous section. This can be the kind of technology that powers the next generation of recruitment software. It's not hard to imagine those software discriminating against women when searching for and selecting candidates for a programming job.

For instance, separate reports recently showed that both Google's and LinkedIn's platforms were showing high-paying job ads more frequently to men than women.

Algorithmic bias can have even more damaging effect in other areas such as law enforcement. In 2016, a ProPublica investigation found that an AI-powered tool used by law enforcement was more likely to declare black people as under the high risk of recidivism than white people. In some states, judges rely on such tools to decide who stays in jail and who walks free, sometimes without doing further investigation themselves.

Similar cases can happen in other areas such as loan approval, where people who are underrepresented will be further marginalized and deprived of service. In healthcare, where AI is making great inroads in diagnosing and curing diseases, algorithms can harm populations whose data has not been included in the training sets.

In fact, if not addressed, algorithmic bias can lead to the amplification of human biases. Under the illusion that software isn't biased, humans tend to trust the judgement of AI algorithms, oblivious that those judgments are already reflecting their own prejudices. As a result, we will accept AI-driven decisions without doubting them and create more biased data for those algorithms to further "enhance" themselves on.

How to fight algorithmic bias?

The first step to avoiding algorithmic bias is to acknowledge the limits of artificial intelligence. Deep learning algorithms are not racist, but we are, and they will pick up whatever biases we intentionally or absentmindedly have.

Knowing this, we need to take measures to make sure the data we feed to our algorithms is diversified, especially when developing applications that make decisions that can have severe repercussions on the lives and health of the people who directly or indirectly use them. There are a handful of efforts that use statistical methods to spot hidden biases in algorithms.

Another necessary step is for companies that develop AI applications to be more transparent about their products. Presently, most companies tend to hide their algorithms' inner workings as trade secrets. This makes it difficult to scrutinize those algorithms and find potential pain points.

We also need to address AI's black box problem. When deep learning algorithms become too complicated, finding the reasons behind their decisions becomes very difficult. Not knowing how an algorithm reached a conclusion can make it hard to find and counter biased functionality. This too is an area where several organization, including the U.S. Defense Department's Advanced Research Projects Agency (DARPA), are leading efforts to make deep learning algorithms open to scrutiny or self-explainable.

At the end of the day algorithmic bias is a human problem, not a technical one, and the real solution is to start removing bias in every aspect of our personal and social lives. This means endorsing diversity in employment, education, politics and more. If we want to fix our algorithms, we should start by fixing ourselves.

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This article is part of Demystifying AI, a series of posts that (try) to disambiguate the jargon and myths surrounding AI. In early 2016, Microsoft launched Tay, an AI chatbot that was supposed to mimic the behavior of a curious teenage girl and engage in smart discussions with Twitter users.