With Great Power: AI and Equity

The Northeast Chapter of the Association for Information Science & Technology (NEASIS&T) and the Simmons University Student Chapter (Simmons ASIS&T), with additional support of the ASIS&T SIG-AI, invite you to join us (virtually!) at our 2022 Annual Conference.

Date: Friday, March 25, 2022, 9:30 a.m.- 3:50 p.m. EST

Where: Online via Zoom (link will be provided to registrants prior to event)

Cost: Free for ASIS&T members; $30 for General Admission (non-student); $15 for Students (non-ASIST) Admission. If registering a group of 4 or more for general admission for a single organization, there will be a 20% discount per general admission ticket. Please reach out to neasist@gmail.com to arrange for a special code and instructions before registering.

This event looks at equity considerations when creating and deploying artificial intelligence (AI) and how we can be intentional about our use of artificial intelligence to do good and remove barriers.

AI is already very deeply entrenched in our lives. AI is deployed every time computing technology is used to help make decisions or do any of the million things that replicate human behavior. The intelligence component of AI derives from humans pouring massive amounts of data for the machine to learn, analyze, and create recognizable patterns and it reflects the best and worst of us. AI has made our lives easier in many ways; it has also perpetuated existing harmful biases and barriers which must be addressed. As the adage goes: “with great power comes great responsibility”.

Schedule

Please note that the schedule is subject to change up to the day of the conference. All times listed are EST.

9:30-9:40 AM: Welcome & Conference Logistics from Jay Bowling (NEASIS&T) and Nick Bodanza (Simmons ASIS&T)

9:40 AM -10:30 AM: Morning Keynote, "Fairness and Equity in Computing: Challenges and Possibilities" - Marie desJardins

10:30 AM - 10:40 AM: Break

10:40 AM - 11:00 AM: "AI and Socially Vulnerable Populations" - Jenny Yuan

11:00 AM - 11:20 AM: “AI, Accountability, and Law” - David Turetsky

11:20 AM - 11:40 AM: "Rethinking Conceptualizations of Algorithmic Fairness in Public Sector Domains: An Algorithmic Policing Perspective " - Emmanuel Udoh

11:40 AM - 12:00 PM: "Social Justice Issues: Artificial Intelligence for Emergency Management" - DeeDee Bennett-Gayle

12:00 PM - 12:20 PM: Morning Speakers panel with Jenny Yuan, David Turetsky, Emmanuel Udoh, DeeDee Bennett-Gayle, and Marie desJardins , moderated by Rong Tang

12:20 PM - 12:50 PM: Lunch Break

12:50 PM - 1:00 PM: NEASIS&T & Simmons ASIS&T Update (Jay Bowling, Bill Lundmark, and Jennifer Sunoo)

1:00 PM -1:50 PM: Afternoon Keynote, ""Responsible AI: We Can't Assume What We Haven't Tested"" - Nashlie Sephus,

1:50 PM - 2:00 PM: Afternoon Break

2:00 PM - 2:20 PM: "Algorithmic Information Access is the New Smoking" - Chirag Shah

2:20 PM - 2:40 PM: "AI for Everyone" - Indrani Mandal

2:40 PM - 3:00 PM: "A Discovery Process for Spotting Structural Inequalities in AI Systems" - Hong Qu

3:00 PM - 3:20 PM: "Libraries, Learning Analytics, and the Ethics of “Student Success” Systems in Higher Education" - Kevin Kidd

3:20 PM - 3:40 PM: Afternoon Speakers panel with Indrani Mandal, Hong Qu, and Kevin Kidd, moderated by Chris Kaufman

3:40 PM - 3:50 PM: Conference Wrap-up by NEASIS&T Program Committee

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Additional Information for Attendees

Zoom link will be provided to registrants 24 hours prior to the conference. And reminder sent 3 hours before conference.

Live captioning through Zoom will be available for conference sessions.

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Details

(more will be added)

9:40 AM -10:30 AM: Morning Keynote, "Fairness and Equity in Computing: Challenges and Possibilities" - Marie desJardins

Data science, machine learning, and artificial intelligence are among the fastest-growing technologies of the 21st century. The rapid adoption of AI and data-driven decision making will have a substantial impact on job availability, privacy and security, education at all levels, business operations, accessibility of information, scientific activity, and how we interact with each other and with the world around us. The potential challenges of this transformation include data bias, misinformation and disinformation, privacy and security concerns, and autonomy and accountability. In order to ensure that all members of society benefit equally from these advances in technology, it is essential for the industry to recognize, understand, and mitigate against potential negative consequences. One of the best ways to accomplish these goals is to increase the diversity of the engineers who create these systems.

Dr. desJardins will talk about the current state of AI, machine learning, and data science; the future of AI technology; the importance of diversity for creating robust, effective engineering solutions; and how we can thoughtfully ensure that these technologies will positively affect our lives and the lives of generations to come.

10:40 AM - 11:00 AM: "AI and Socially Vulnerable Populations" - Jenny Yuan

Jenny Yuan is an associate professor in the College of Emergency Preparedness, Homeland Security and Cybersecurity from the University at Albany, State University of New York. She will talk about a book they recently edited, and a chapter on perception and adoption of AI technologies by socially vulnerable populations.

11:00 AM - 11:20 AM: “AI, Accountability, and Law” - David Turetsky

Often, technology changes faster than the law adapts. This presentation addresses aspects of the emerging intersection of law, AI and accountability.”

David Turetsky is Professor of Practice at the University at Albany’s College of Emergency Preparedness, Homeland Security and Cybersecurity, and teaches in areas including information policy, leadership and ethics, and cybersecurity. He has held senior positions in government, law practice and business.

11:20 AM - 11:40 AM: "Rethinking Conceptualizations of Algorithmic Fairness in Public Sector Domains: An Algorithmic Policing Perspective " - Emmanuel Udoh

A common thread running through most definitions/conceptualizations of algorithmic fairness is that of “the state of an algorithmic decision model being free of intentional bias”. However, the concept needs a more comprehensive conceptualization, and using algorithmic policing as an example, this paper highlights the components of a broader, more comprehensive conceptualization.

11:40 AM - 12:00 PM: "Social Justice Issues: Artificial Intelligence for Emergency Management" - DeeDee Bennett-Gayle

This presentation will introduce the audience to emergency management, current challenges, and various proposed uses in which AI can be helpful. Throughout the presentation, elements of social justice issues that are present across the life cycle of disasters and in previous use of AI for related fields are introduced. The discussion presents questions to consider to assure equity in preparedness, response, recovery, and mitigation efforts for disasters. The presenter will also introduce ongoing efforts in research and practice, to increase awareness about social justice concerns.

1:00 PM - 1:50PM: Afternoon Keynote, ""Responsible AI: We Can't Assume What We Haven't Tested"" - Nashlie Sephus

Experiments in bias checks have emerged more and more over the years as pioneers' influence challenges the traditional technology development life cycle. Dr. Nashlie Sephus discusses principles and best practices to keep in mind for consumers and developers of AI along with examples from the field. She also discusses ways to diversify tech teams and lend to better, more inclusive products.

Dr. Nashlie Sephus is the Tech Evangelist for Amazon AI focusing on fairness and identifying biases at AWS AI. She formerly led the Amazon Visual Search team as an Applied Scientist in Atlanta, which launched visual search for replacement parts on the Amazon Shopping app in June 2018. In 2018, Dr. Sephus became the founder and CEO of The Bean Path non-profit organization based in Jackson, MS assisting individuals and startups with technical expertise and guidance. In September of 2020, she became the owner and developer of the Jackson Tech District—14-acres of mixed-use commercial real-estate in downtown Jackson—to bring tech training and workforce/economic development to the area.

2:00 PM - 2:20 PM: "Algorithmic Information Access is the New Smoking" - Chirag Shah

It is increasingly difficult to access any information today that is not mediated by some algorithm behind the scenes — whether it’s a search engine or a recommender system. Since people often trust these systems and the information they provide, their actions continue feeding into such systems as validating signals. This creates a positive feedback loop that is hard to break, but can be possible if we focus on changes with users, systems, and society, as well as through regulations — much like how we have curbed the tobacco consumption.

Chirag Shah is an Associate Professor in the iSchool at University of Washington. He is the Founding Co-Director of Responsibility in AI Systems & Experiences (RAISE), Founding Chair of ASIS&T SIG AI, and Founding Editor-in-Chief of Information Matters.

2:20 PM - 2:40 PM: "AI for Everyone" - Dr. Indrani Mandal

This talk discusses AI education and research efforts led by the URI AI lab: Summer camps and Internships. The summer camps were designed to focus on students from underserved communities. The Internships are focused on students with diverse backgrounds.

2:40 PM - 3:00 PM: "A Discovery Process for Spotting Structural Inequalities in AI Systems" - Hong Qu

The risk of delegating high-stakes decisions to AI exposes everyone to unequal treatment because these seemingly impartial algorithms are the product of harmful data and practices that may amplify historical biases in society. Fairness requires vigilance and accountability from stakeholders at every stage of AI lifecycles. We propose the AI Blindspot toolkit for advancing equity in AI systems.

Hong Qu is an adjunct lecturer at Harvard Kennedy School. He is also an affiliate fellow at the Stanford Institute for Human-Centered Artificial Intelligence (HAI) and a PhD candidate at the Network Science Institute at Northeastern University

3:00 PM - 3:20 PM: "Libraries, Learning Analytics, and the Ethics of “Student Success” Systems in Higher Education" - Kevin Kidd

The past 15 years has seen the rapid growth of Learning Analytics systems in higher education. A broad definition of Learning Analytics (LA) can be taken from the call for papers of the first International Conference on Learning Analytics and Knowledge (LAK 2011), and subsequently adopted by the Society for Learning Analytics Research (SoLAR): “Learning analytics is the measurement, collection, analysis and reporting of data about learners and their contexts, for purposes of understanding and optimising learning and the environments in which it occurs.” The design of Learning Analytics systems technologies is inspired by the growth of algorithm-enabled “big data” analysis and AI. A variety of financial, political, and social pressures has led to widespread adoption of such systems in U.S. institutions of higher education. The fact that LA systems are designed to aggregate and analyze an enormous amount of what is nominally private student information, raises a number of legitimate concerns among some librarians and educators about widespread adoption of LA in institutes of higher education. Concerns include the fact that (1) surveillance of student behaviors is a fundamental violation of their right to privacy; that (2) private student data might be accessed by individuals who are not trained to use it, who might use it unethically, or who might not have a legitimate right to view that data; that (3) many LA systems are cloud-based; therefore, private student data, protected by FERPA and other federal laws, is held by 3rd parties; that (4) students might be improperly profiled or otherwise pigeonholed by LA algorithms in a way that reinforces racial, gender, sexual or other stereotypes; and that (5) the benefits of systematic surveillance and collection of data that reveal students’ intellectual behaviors and interests actually redound to the institution, rather than to the student, and may in fact be a violation of students’ intellectual freedom. This talk will provide an overview of the ethical quandaries raised by the adoption of Learning Analytics in higher education and subsequent pressures placed on libraries to share data about students’ usage of the Library.

Kevin is Dean of the Maxwell Library at Bridgewater State University and is a SLIS Ph.D. student in Library & Information Science at Simmons University.