# data[].attributes.body
Internal alignment around AIR focus on AI- our commitments, our DEI values, and our market priorities.

# data[].attributes.body
The exclusion of "traditional" perspectives in the generation of AI.

# data[].attributes.body
Threats to grow of staff and careers based on exclusion of new/different talent

# data[].attributes.body
Limitations of the tools to detect nuances and cultural differences

# data[].attributes.body
Unequal Access to AI technology

# data[].attributes.headline
AI may amplify existing equity gaps; AI gives appearance of objectivity; not everyone’s voices are reflected (in the training sets); lack of mathematical models underlying newest AI tools

# data[].attributes.subject
AI may amplify existing equity gaps; AI gives appearance of objectivity; not everyone’s voices are reflected (in the training sets); lack of mathematical models underlying newest AI tools

# data[].attributes.headline
Responsible use of data to improve data collection processes and outcomes 

# data[].attributes.subject
Responsible use of data to improve data collection processes and outcomes 

# data[].attributes.headline
Exhibit leadership in socially responsive ways and lead in AI best practices

# data[].attributes.subject
Exhibit leadership in socially responsive ways and lead in AI best practices

# data[].attributes.headline
We have a capacity through AI to broaden our reach to new settings, multi-lingual settings, partners, communities and new sectors

# data[].attributes.subject
We have a capacity through AI to broaden our reach to new settings, multi-lingual settings, partners, communities and new sectors

# data[].attributes.headline
AIR’s reputation and eminence could be threatened by low-quality products; competition with firms like us but which may have VC; need to build AI expertise; competition for talent; collective human intelligence can lead to negative feedback loops

# data[].attributes.subject
AIR’s reputation and eminence could be threatened by low-quality products; competition with firms like us but which may have VC; need to build AI expertise; competition for talent; collective human intelligence can lead to negative feedback loops

# data[].attributes.headline
Cultivate innovation spaces, explicit environments for learning, reinforce an environment where were test and sandbox ideas

# data[].attributes.subject
Cultivate innovation spaces, explicit environments for learning, reinforce an environment where were test and sandbox ideas

# data[].attributes.headline
Build understanding of context and sharing that and understanding implications

# data[].attributes.subject
Build understanding of context and sharing that and understanding implications

# data[].attributes.body
-Quality suffers from moving to fast
-Quality suffers from moving to slow. We get left behind
-Not having the data we need to speak about communities of interest

# data[].attributes.headline
Black box problem makes assessing bias difficult or impossible; AI is easy to use incorrectly; need to balance mission focus with need to stay on cutting edge

# data[].attributes.subject
Black box problem makes assessing bias difficult or impossible; AI is easy to use incorrectly; need to balance mission focus with need to stay on cutting edge

# data[].attributes.headline
Elite creators and users create even more privilege and disparity/lack of access to data and information 

# data[].attributes.subject
Elite creators and users create even more privilege and disparity/lack of access to data and information 

# data[].attributes.headline
Creating Safe Space to Change what is/not done by humans

# data[].attributes.subject
Creating Safe Space to Change what is/not done by humans

# data[].attributes.body
-Monitoring AIR results
-How to build skills in AIR staff (current and new)

# data[].attributes.headline
Lack of control or understanding / knowledge of quality of data - how do we assess rigor?

# data[].attributes.subject
Lack of control or understanding / knowledge of quality of data - how do we assess rigor?

# data[].attributes.headline
If we cannot assess quality of data used in AI or results, what are the implications for rigor, transparency, and compounded bias. 

# data[].attributes.subject
If we cannot assess quality of data used in AI or results, what are the implications for rigor, transparency, and compounded bias. 

# data[].attributes.headline
opportunities to become leaders and protect marginalized populations; AI can be used for good causes (e.g., clinical decision support, early ID of dyslexia); opportunity to set standards for ethical AI

# data[].attributes.subject
opportunities to become leaders and protect marginalized populations; AI can be used for good causes (e.g., clinical decision support, early ID of dyslexia); opportunity to set standards for ethical AI

# data[].attributes.body
Once "clear" terms being undefined (e.g., equity, researcher). For example, who is a researcher if all have access to AI.

# data[].attributes.headline
How far down the inequity trail are we when it comes to the challenges that AI has already presented related to DEI?

# data[].attributes.subject
How far down the inequity trail are we when it comes to the challenges that AI has already presented related to DEI?

# data[].attributes.headline
staff getting marginalized or left behind if they lack expertise or professional learning opportunities 

# data[].attributes.subject
staff getting marginalized or left behind if they lack expertise or professional learning opportunities 

# data[].attributes.headline
reduced learning opportunities for staff who rely too heavily on AI (e.g., never learning how to conduct a real lit review)

# data[].attributes.subject
reduced learning opportunities for staff who rely too heavily on AI (e.g., never learning how to conduct a real lit review)

# data[].attributes.body
What counts as data, how is it being used, how is it gathered, how to collect more than easy-to-get ("cheap")for research

# data[].attributes.headline
If elite are creating AI, it's THEIR biases that are baked into it and are being perpetuated. 

# data[].attributes.subject
If elite are creating AI, it's THEIR biases that are baked into it and are being perpetuated. 

# data[].attributes.headline
AI work could be incongruous with local, regional, tribal, or national norms

# data[].attributes.subject
AI work could be incongruous with local, regional, tribal, or national norms

# data[].attributes.headline
some roles (e.g., RA) might need to change significantly

# data[].attributes.subject
some roles (e.g., RA) might need to change significantly

# data[].attributes.headline
could focus too much on quant data and ignore the voices of people with lived experience 

# data[].attributes.subject
could focus too much on quant data and ignore the voices of people with lived experience 

# data[].attributes.body
Access and democratization of data/information.

# data[].attributes.body
Synthetic data sets
Speed to task automation

# data[].attributes.body
-Data collection of underrepresented communities
-thought leaders on what to measure

# data[].attributes.headline
using data of individuals without adequate privacy protections or compensation

# data[].attributes.subject
using data of individuals without adequate privacy protections or compensation

# data[].attributes.headline
reputational harm from bad AI applications or lack of oversight of what AI produces

# data[].attributes.subject
reputational harm from bad AI applications or lack of oversight of what AI produces

# data[].attributes.body
Increase opps for staff to focus on AI and expand career path.

# data[].attributes.headline
Deeper Insights into sub-groups of populations

# data[].attributes.subject
Deeper Insights into sub-groups of populations

# data[].attributes.headline
AI could erode the perceive value of our more careful, rigorous, vetted (and expensive) analysis

# data[].attributes.subject
AI could erode the perceive value of our more careful, rigorous, vetted (and expensive) analysis

# data[].attributes.body
Internal alignment and collaboration across silos through the lens of DEI and CLC.

# data[].attributes.headline
AI, bad data, deepfakes etc. could erode political and public trust in evidence including ours

# data[].attributes.subject
AI, bad data, deepfakes etc. could erode political and public trust in evidence including ours

# data[].attributes.headline
Train staff on base knowledge of AI for internal equity including inviting external experts to support our professional learning

# data[].attributes.subject
Train staff on base knowledge of AI for internal equity including inviting external experts to support our professional learning

# data[].attributes.body
Eminence/Leading the field/setting best practices

# data[].attributes.headline
strengthen our influence by leading the conversation, not following others' lead

# data[].attributes.subject
strengthen our influence by leading the conversation, not following others' lead

# data[].attributes.headline
leverage AI to make our dissemination products more useful, innovative, efficient

# data[].attributes.subject
leverage AI to make our dissemination products more useful, innovative, efficient

# data[].attributes.headline
Be open to the fact that how AI is used may not be the same across domains - some domains may be farther along than others

# data[].attributes.subject
Be open to the fact that how AI is used may not be the same across domains - some domains may be farther along than others

# data[].attributes.body
What equity questions do we want to answer related to AI

# data[].attributes.headline
use AI in ways that increase equity of access to opportunity (e.g., writing supports for speakers of languages other than English)

# data[].attributes.subject
use AI in ways that increase equity of access to opportunity (e.g., writing supports for speakers of languages other than English)

# data[].attributes.body
Position ourselves to take the work that the machine cannot do for example collect data about communities that are represented in data sets that AI tools are using

# data[].attributes.headline
Develop transparent policies about how we use AI and believe it SHOULD be used for our work

# data[].attributes.subject
Develop transparent policies about how we use AI and believe it SHOULD be used for our work

# data[].attributes.headline
become THE leader in ethnical, effective AI use in research, TA, & training

# data[].attributes.subject
become THE leader in ethnical, effective AI use in research, TA, & training

# data[].attributes.headline
more data/analytics to spark self-reflection and development

# data[].attributes.subject
more data/analytics to spark self-reflection and development

# data[].attributes.headline
Consider a research and development approach where we can sandbox possible uses of AI to improve equity in the field

# data[].attributes.subject
Consider a research and development approach where we can sandbox possible uses of AI to improve equity in the field

# data[].attributes.headline
partner with clients to help them adopt their own approach to AI

# data[].attributes.subject
partner with clients to help them adopt their own approach to AI

# data[].attributes.headline
reduce time and money spent on tedious/mundane tasks, freeing staff up to focus more on intellectually rewarding and impactful work

# data[].attributes.subject
reduce time and money spent on tedious/mundane tasks, freeing staff up to focus more on intellectually rewarding and impactful work

# data[].attributes.headline
Ensure that humans are the drivers of create inference and decisions regardless of what data and computing we allow AI to do for us

# data[].attributes.subject
Ensure that humans are the drivers of create inference and decisions regardless of what data and computing we allow AI to do for us

# data[].attributes.headline
expand the research we're doing by exploring new pursuits related to AI

# data[].attributes.subject
expand the research we're doing by exploring new pursuits related to AI

# data[].attributes.headline
Study tension between removing bias in decision making, such as in the justice space, and the fact that humans introduce the bias into the decision making tools and algorithms 

# data[].attributes.subject
Study tension between removing bias in decision making, such as in the justice space, and the fact that humans introduce the bias into the decision making tools and algorithms 

# data[].attributes.headline
Determine how to interrogate data from a quality assurance and adjustment standpoint

# data[].attributes.subject
Determine how to interrogate data from a quality assurance and adjustment standpoint

# data[].attributes.headline
Position AIR as experts in assessing / judging validity of AI tools (example - Kimetrica tool that used facial mapping to determine malnutrition (tool had inherent biases based on small sample of Kenyan population used for development of tool)

# data[].attributes.subject
Position AIR as experts in assessing / judging validity of AI tools (example - Kimetrica tool that used facial mapping to determine malnutrition (tool had inherent biases based on small sample of Kenyan population used for development of tool)

# data[].attributes.body
of where we are going that we can use to prioritize our investments. What do we want AIR to be known for in the AI space?

# data[].attributes.headline
Training staff; providing guidelines / standards for AI use and disclosures; identify pain points so you can address them; team with appropriate partners; case studies/examples of good and bad AI as basis for seminars; be sure we can ensure reproducibility

# data[].attributes.subject
Training staff; providing guidelines / standards for AI use and disclosures; identify pain points so you can address them; team with appropriate partners; case studies/examples of good and bad AI as basis for seminars; be sure we can ensure reproducibility

# data[].attributes.body
Engage staff in AIR's embracing of AI, and engage more staff across all levels and functions.

# data[].attributes.body
Convene partners and thought leaders in alignment with our strategy around AI.

# data[].attributes.body
Develop and implement training and knowledge sharing opps for staff.

# data[].attributes.body
Design and implement strategy, and policies, around the use of AI.

# data[].attributes.headline
consistently check our work for potential biases

# data[].attributes.subject
consistently check our work for potential biases

# data[].attributes.headline
use AI transparently for staffing/HR practices, could help support staff who would otherwise be overlooked

# data[].attributes.subject
use AI transparently for staffing/HR practices, could help support staff who would otherwise be overlooked

# data[].attributes.headline
Develop standards and guidelines for public dissemination

# data[].attributes.subject
Develop standards and guidelines for public dissemination

# data[].attributes.headline
develop standards and guidelines to guide AIR's work

# data[].attributes.subject
develop standards and guidelines to guide AIR's work

# data[].attributes.headline
Identify points of contact and mentors with expertise to support pursuits & projects

# data[].attributes.subject
Identify points of contact and mentors with expertise to support pursuits & projects

# data[].attributes.headline
do a scan of what our competitors are doing

# data[].attributes.subject
do a scan of what our competitors are doing

# data[].attributes.headline
identify and join networks and coalitions equally committed to adopting AI in an equitable way

# data[].attributes.subject
identify and join networks and coalitions equally committed to adopting AI in an equitable way

# data[].attributes.headline
develop test beds to use & impact of AI, both positive and negative

# data[].attributes.subject
develop test beds to use & impact of AI, both positive and negative

# data[].attributes.headline
put out some papers soon on topics such as equity issues, how to protect privacy, what we know about bias in hiring etc.

# data[].attributes.subject
put out some papers soon on topics such as equity issues, how to protect privacy, what we know about bias in hiring etc.

# data[].attributes.headline
Ensure L2s/L3s are included in AI learning opportunities and strategy planning

# data[].attributes.subject
Ensure L2s/L3s are included in AI learning opportunities and strategy planning

# data[].attributes.headline
train staff at all levels about what AI is, what we can do with it, risks & challenges

# data[].attributes.subject
train staff at all levels about what AI is, what we can do with it, risks & challenges

# data[].attributes.headline
engage in continued cross-division conversations on best practices

# data[].attributes.subject
engage in continued cross-division conversations on best practices

# data[].attributes.body
AIR's traditional research not very conducive
for predictive analysis

# data[].attributes.body
How can we be more creative instead of less start

# data[].attributes.body
De-humanization of staff, work, etc.

# data[].attributes.body
Not having full understanding and impact of AI on our ability to deliver on our mission.

# data[].attributes.body
Use mission focus to become leader and have voice / promote voice for underserved populations

# data[].attributes.body
Develop new offerings and expand into new areas

# data[].attributes.body
Develop new offerings and expand into new areas

# data[].attributes.body
Development of standards, guidelines and policies

# data[].attributes.body
Recruiting aligned to AIR mission = need to look for staff "affiliated" with our mission.

# data[].attributes.body
Partnership - Universities, peers

# data[].attributes.body
Strong follow-up to the session - 3 mos? 6 mos?

# data[].attributes.body
Need to continue to have discussions and create an "AIR AI" POV

# data[].attributes.body
Understand how our staff is using AI - today and do we need policies to govern

# data[].attributes.body
Inventory of decision support tools build by AIR

# data[].attributes.body
Integrate AI in a way that all staff; entry level, mid-level, senior, and tenure staff can understand and implement AI. Allow room for transition time it will take for staff to adjust. Possibly build it out in iterations.

# data[].attributes.headline
Potential threat is focused on Staff - AI could dehumanize staff/work, staff could be left behind if they don't have the training, roles would need to change significantly, could rely heavily on AI/reduce creativity.

# data[].attributes.subject
Potential threat is focused on Staff - AI could dehumanize staff/work, staff could be left behind if they don't have the training, roles would need to change significantly, could rely heavily on AI/reduce creativity.

# data[].attributes.body
How to ensure that marginalized populations have access to AI, AI based tools etc. (just remembering how some students did not have computers to access virtual instruction).