# data[].attributes.body
How vulnerable populations (e.g., children, youth, patients) in AIR's sectors or connected to AIR clients and projects are subject to ethical or unethical applications of AI.

# data[].attributes.body
Mentoring and training of researchers in AI methods, including intentional opportunities through projects and through overhead investments

# data[].attributes.body
Lead consortium of similar orgs (e.g. MPR, Westat, RTI, NORC, etc.) on AI policy making. Look at new orgs also to be part of consortium.

# data[].attributes.headline
Hire people with expertise in the intersection of AI and communications.

# data[].attributes.subject
Hire people with expertise in the intersection of AI and communications.

# data[].attributes.headline
Engage with clients around their interests and understanding of AI.

# data[].attributes.subject
Engage with clients around their interests and understanding of AI.

# data[].attributes.headline
Provide clear guidelines knowledge sharing and discussion forums around AI.

# data[].attributes.subject
Provide clear guidelines knowledge sharing and discussion forums around AI.

# data[].attributes.headline
Unintended bias in the generation of surveys.

# data[].attributes.subject
Unintended bias in the generation of surveys.

# data[].attributes.headline
 Perceptions vs misperceptions of AI and how it leads to overconfidence in the results and/or being misled by bad actors.

# data[].attributes.subject
 Perceptions vs misperceptions of AI and how it leads to overconfidence in the results and/or being misled by bad actors.

# data[].attributes.headline
Machine learning can make data analysis too easily accessible potentially reducing the need or statistical experts like AIR.

# data[].attributes.subject
Machine learning can make data analysis too easily accessible potentially reducing the need or statistical experts like AIR.

# data[].attributes.headline
Negative impact on AIR pursuits due to less RFPs, and competitors under cutting costs.

# data[].attributes.subject
Negative impact on AIR pursuits due to less RFPs, and competitors under cutting costs.

# data[].attributes.headline
Learn/explore new technology before committing to anything new and partner w/ appropriate companies to develop the technologies

# data[].attributes.subject
Learn/explore new technology before committing to anything new and partner w/ appropriate companies to develop the technologies

# data[].attributes.headline
Commit to NOT engage in AI arms race with competitors

# data[].attributes.subject
Commit to NOT engage in AI arms race with competitors

# data[].attributes.headline
Partner with like-minded organizations to harness our collective expertise to influence policy 

# data[].attributes.subject
Partner with like-minded organizations to harness our collective expertise to influence policy 

# data[].attributes.headline
Establish AIR as thought leaders in using AI for evidence generation 

# data[].attributes.subject
Establish AIR as thought leaders in using AI for evidence generation 

# data[].attributes.headline
Diversify our workforce further to contribute even more to AI strategy

# data[].attributes.subject
Diversify our workforce further to contribute even more to AI strategy

# data[].attributes.headline
Continue to engage cross-division staff in ideation about AI use at AIR

# data[].attributes.subject
Continue to engage cross-division staff in ideation about AI use at AIR

# data[].attributes.headline
As we disseminate results of our work, identify opportunities to introduce visually appealing recognition to various methods used and why

# data[].attributes.subject
As we disseminate results of our work, identify opportunities to introduce visually appealing recognition to various methods used and why

# data[].attributes.headline
educate policy makers on the limitations of AI-generated information

# data[].attributes.subject
educate policy makers on the limitations of AI-generated information

# data[].attributes.headline
Support or even lead policy development to influence regulation/application of AI in the field

# data[].attributes.subject
Support or even lead policy development to influence regulation/application of AI in the field

# data[].attributes.headline
Find ways to use AI & big data to validate our research evidence

# data[].attributes.subject
Find ways to use AI & big data to validate our research evidence

# data[].attributes.headline
Model transparency in the tools we develop (such as assumptions, stakeholder engagement in development, etc). Publish information about the limitations of tools and data we use as a way to share our learnings with the field.

# data[].attributes.subject
Model transparency in the tools we develop (such as assumptions, stakeholder engagement in development, etc). Publish information about the limitations of tools and data we use as a way to share our learnings with the field.

# data[].attributes.headline
Develop and test our own AI solutions to identify best practices

# data[].attributes.subject
Develop and test our own AI solutions to identify best practices

# data[].attributes.headline
Train staff on the benefits and risks of AI 

# data[].attributes.subject
Train staff on the benefits and risks of AI 

# data[].attributes.headline
Find niches where AIR has competitive advantage for project pursuits

# data[].attributes.subject
Find niches where AIR has competitive advantage for project pursuits

# data[].attributes.headline
AI can help us be faster and more efficient with our AI-generated or augmented work. 

# data[].attributes.subject
AI can help us be faster and more efficient with our AI-generated or augmented work. 

# data[].attributes.headline
Leading in generating evidence using AI will help us position ourselves as scholarly leaders in AI use.

# data[].attributes.subject
Leading in generating evidence using AI will help us position ourselves as scholarly leaders in AI use.

# data[].attributes.headline
Use of AI to augment our productivity will free up staff time for more generative thinking and work. 

# data[].attributes.subject
Use of AI to augment our productivity will free up staff time for more generative thinking and work. 

# data[].attributes.headline
We may be able to use AI to generate new hypotheses and theories we could not generate through our own thinking. 

# data[].attributes.subject
We may be able to use AI to generate new hypotheses and theories we could not generate through our own thinking. 

# data[].attributes.headline
Effective use of AI may help us reveal and address inequities that were hidden by human-led methods and approaches.

# data[].attributes.subject
Effective use of AI may help us reveal and address inequities that were hidden by human-led methods and approaches.

# data[].attributes.body
Identify offramps, decision-points, milestones and use those to make adjustments as needed

# data[].attributes.headline
Data from AI processes are only as valid, accurate, and strong as the humans that write the code. "garbage in, garbage out"

# data[].attributes.subject
Data from AI processes are only as valid, accurate, and strong as the humans that write the code. "garbage in, garbage out"

# data[].attributes.headline
There are currently few policies and guardrails in place for AI that are nationally or internationally accepted and adhered to

# data[].attributes.subject
There are currently few policies and guardrails in place for AI that are nationally or internationally accepted and adhered to

# data[].attributes.headline
Concern that AI may reduce the need for the "human" part of our work - will our jobs be replaced or no longer needed?

# data[].attributes.subject
Concern that AI may reduce the need for the "human" part of our work - will our jobs be replaced or no longer needed?

# data[].attributes.headline
Perceived nimbless of AIR of staff, in terms of shifting our work and compared to our competitors 

# data[].attributes.subject
Perceived nimbless of AIR of staff, in terms of shifting our work and compared to our competitors 

# data[].attributes.headline
Automating / extending data stories / reporting

# data[].attributes.subject
Automating / extending data stories / reporting

# data[].attributes.body
- will clients choose AIR when they can choose AI

# data[].attributes.body
Change management (external and internal)

# data[].attributes.headline
Using more data; processing wider data sets

# data[].attributes.subject
Using more data; processing wider data sets

# data[].attributes.body
Sometimes more may not be better; studying under which conditions that may be the case

# data[].attributes.body
Establish common language and deeper understanding. Provide/share examples of what we have done.

# data[].attributes.body
- more data does not equal better info
- data applicable to sector? community?

# data[].attributes.body
Assessing where our clients are in terms of AI.

# data[].attributes.headline
Becoming an influencer for standards, policy to stakeholders

# data[].attributes.subject
Becoming an influencer for standards, policy to stakeholders

# data[].attributes.headline
AI has the potential to dumb down humans who are reliant on AI. 

# data[].attributes.subject
AI has the potential to dumb down humans who are reliant on AI. 

# data[].attributes.body
Develop a policy (internal and external) to tell our stance on use of AI

# data[].attributes.body
- lack of transparency with AI
- skill to interpret AI - generated results

# data[].attributes.headline
Clients and public may distrust our work. 

# data[].attributes.subject
Clients and public may distrust our work. 

# data[].attributes.body
Setting guardrails - e.g., legal constraints and considerations, understanding our clients' guardrails

# data[].attributes.headline
AI ignores the "why" and "theory" and amplifies inherent limitations of quantitative focus of AI. 

# data[].attributes.subject
AI ignores the "why" and "theory" and amplifies inherent limitations of quantitative focus of AI. 

# data[].attributes.body
- will our reputation be strong in new areas
- risk devaluing our reputation
- managing errors

# data[].attributes.body
Capabilities of staff and their skills - e.g., hiring, training, inventory of current skills, openness to diverse teams with different skills.

# data[].attributes.headline
Potential for increased bias showing up in our work. 

# data[].attributes.subject
Potential for increased bias showing up in our work. 

# data[].attributes.body
Conduct faster processing of data

# data[].attributes.headline
Market scan, analysis of competitors' work and the work of other orgs (e.g., vendors)

# data[].attributes.subject
Market scan, analysis of competitors' work and the work of other orgs (e.g., vendors)

# data[].attributes.headline
Staff training (all staff, not just specialists), internships and other approaches to building human capital

# data[].attributes.subject
Staff training (all staff, not just specialists), internships and other approaches to building human capital

# data[].attributes.headline
Unsure if AIR has critical mass of internal expertise to make effective use of AI. 

# data[].attributes.subject
Unsure if AIR has critical mass of internal expertise to make effective use of AI. 

# data[].attributes.headline
Harness collective expertise of staff to design solutions to threats. 

# data[].attributes.subject
Harness collective expertise of staff to design solutions to threats. 

# data[].attributes.headline
Create and/or sign onto policies that provide guidance and guardrails for AI use and application.

# data[].attributes.subject
Create and/or sign onto policies that provide guidance and guardrails for AI use and application.

# data[].attributes.headline
Apply current tools to completed analyses to see if we get different results or learn anything new.

# data[].attributes.subject
Apply current tools to completed analyses to see if we get different results or learn anything new.

# data[].attributes.headline
Major themes: training staff, providing guidelines 

# data[].attributes.subject
Major themes: training staff, providing guidelines 

# data[].attributes.headline
risk of racing ahead before we have enough evidence

# data[].attributes.subject
risk of racing ahead before we have enough evidence

# data[].attributes.headline
promote effective, ethical AI use in the communities we serve while mitigating harms

# data[].attributes.subject
promote effective, ethical AI use in the communities we serve while mitigating harms

# data[].attributes.headline
automating some aspects of research (e.g., evidence reviews, qualitative data analysis)

# data[].attributes.subject
automating some aspects of research (e.g., evidence reviews, qualitative data analysis)

# data[].attributes.headline
AIR playing a leading role in setting standards for ethical AI use

# data[].attributes.subject
AIR playing a leading role in setting standards for ethical AI use

# data[].attributes.headline
Opportunities include efficiency, new verticals / creativity, AIR can be leaders in understanding algorithmic bias, can lead in evaluating equity in algorithms

# data[].attributes.subject
Opportunities include efficiency, new verticals / creativity, AIR can be leaders in understanding algorithmic bias, can lead in evaluating equity in algorithms

# data[].attributes.headline
upskilling our staff, building expertise on effective practices

# data[].attributes.subject
upskilling our staff, building expertise on effective practices

# data[].attributes.headline
new approaches to research dissemination (data-viz, etc.)

# data[].attributes.subject
new approaches to research dissemination (data-viz, etc.)

# data[].attributes.headline
Threats include bias, quality, misuse, inaction, privacy

# data[].attributes.subject
Threats include bias, quality, misuse, inaction, privacy

# data[].attributes.headline
enhance our research methodologies, develop innovations to improve the quality of evidence

# data[].attributes.subject
enhance our research methodologies, develop innovations to improve the quality of evidence

# data[].attributes.headline
efficiencies in data collection, analysis, dissemination

# data[].attributes.subject
efficiencies in data collection, analysis, dissemination

# data[].attributes.headline
marginalization of people or groups due to lack of understanding, expertise, training, or resources related to AI

# data[].attributes.subject
marginalization of people or groups due to lack of understanding, expertise, training, or resources related to AI

# data[].attributes.headline
bias & likelihood of perpetuating or exacerbating existing biases

# data[].attributes.subject
bias & likelihood of perpetuating or exacerbating existing biases

# data[].attributes.headline
spread of mis- and disinformation, promoting lack of trust in science & evidence

# data[].attributes.subject
spread of mis- and disinformation, promoting lack of trust in science & evidence

# data[].attributes.headline
lack of the right kind of staff expertise

# data[].attributes.subject
lack of the right kind of staff expertise

# data[].attributes.headline
potential for our competitors to get out ahead of us

# data[].attributes.subject
potential for our competitors to get out ahead of us

# data[].attributes.headline
Become a leader in generating evidence on different effects of AI tools

# data[].attributes.subject
Become a leader in generating evidence on different effects of AI tools