Navigating the Broader Impacts of AI Research: Workshop at NeurIPS 2020
As part of NeurIPS 2020, PAI co-hosted a full day workshop on ‘Navigating the Broader Impacts of AI Research‘. The goal of the event was to encourage the research community to think critically about our roles and responsibilities in anticipating and mitigating potential negative consequences of AI research. It included panel discussions on impact statements, publication norms, harms of AI, and more. The event was part of our ongoing work on responsible publication norms.
We opted to have the majority of the workshop as panel sessions to encourage discussion on what we see as an important topic that requires community participation. There has been a lot of discussion on social media within the AI community on broader impact statements and ethical reflection, but the format is not always conducive to productive disagreement on what is a very nuanced issue. By hosting longer, structured discussions with a diverse set of panelists, we aimed to advance the conversation in an inclusive and productive way.
We were blown away by the thoughtful engagement of the panelists and the attendees, who were able to participate through commenting and asking questions via a text chat function.
We accepted 15 papers to the workshop, and as well as the panel discussions, we also hosted an interactive paper session on GatherTown. This allowed attendees to virtually wander around and speak to authors of the papers that had been accepted to the workshop.
I’d like to thank my co-organizers Carolyn Ashurst (Governance of AI, Future of Humanity Institute), Deb Raji (Mozilla), Solon Barocas (Microsoft), and Stuart Russell (Center for Human-Compatible AI, UC Berkeley), as well as all of our fantastic speakers, panelists, paper authors and reviewers, and all those who helped behind the scenes to make the day a success.
You can watch recordings of the panel sessions and the paper lightning talks below.
Recordings of live panel sessions and talks
Carolyn Ashurst (GovAI) and Rosie Campbell (PAI)
Hanna Wallach (Microsoft)
Panel: Ethical oversight in the peer review process
Sarah Brown (University of Rhode Island), Heather Douglas (Michigan State University), Iason Gabriel (DeepMind, NeurIPS Ethics Advisor), Brent Hecht (Northwestern University, Microsoft). Chaired by Rosie Campbell (PAI).
Panel: Harms from AI research
Anna Lauren Hoffmann (University of Washington), Nyalleng Moorosi (Google AI), Vinay Prabhu (UnifyID), Jake Metcalf (Data & Society), Sherry Stanley (Amazon Mechanical Turk). Chaired by Deborah Raji (Mozilla).
Panel: How should researchers engage with controversial applications of AI?
Cathy O’Neil (O’Neil Risk Consulting & Algorithmic Auditing), Tawana Petty (Stanford University), Cynthia Rudin (Duke University). Chaired by Deborah Raji (Mozilla).
Panel: Responsible publication: NLP case study
Miles Brundage (OpenAI), Bryan McCann (Formerly Salesforce), Colin Raffel (University of North Carolina at Chapel Hill, Google Brain), Natalie Schluter (Google Brain, IT University of Copenhagen), Zeerak Waseem (University of Sheffield). Chaired by Rosie Campbell (PAI).
Panel: Strategies for anticipating and mitigating risks
Ashley Casovan (AI Global), Timnit Gebru (Google), Shakir Mohamed (DeepMind), Aviv Ovadya (Thoughtful Technology Project). Chaired by Solon Barocas (Microsoft).
Panel: The roles of different parts of the research ecosystem in navigating broader impacts
Josh Greenberg (Alfred P. Sloan Foundation), Liesbeth Venema (Nature), Ben Zevenbergen (Google), Lilly Irani (UC San Diego). Chaired by Solon Barocas (Microsoft).
Stuart Russell (CHAI)
Pre-recorded lightning talks of accepted papers
Ideal theory in AI ethics
AI in the “Real World”: Examining the Impact of AI Deployment in Low-Resource Contexts
Nose to Glass: Looking In to Get Beyond
Anticipatory Ethics and the Role of Uncertainty
Auditing Government AI: Assessing ethical vulnerability of machine learning
Like a Researcher Stating Broader Impact For the Very First Time
Overcoming Failures of Imagination in AI Infused System Development and Deployment
Training Ethically Responsible AI Researchers: a Case Study
Biased Programmers? Or Biased Data? A Field Experiment in Operationalizing AI Ethics
The Managerial Effects of Algorithmic Fairness Activism
Ethical Testing in the Real World: Recommendations for Physical Testing of Adversarial Machine Learning Attacks
Analyzing the Machine Learning Conference Review Process
Non-Portability of Algorithmic Fairness in India
An Ethical Highlighter for People-Centric Dataset Creation