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An event co-sponsored by the Centre for Human & Machine Intelligence (HMI),
Frankfurt School of Finance & Management and part of the project group Regulatory Theories of Artificial Intelligence of the Centre Responsible Digitality (ZEVEDI)

This conference, hosted by the Centre for Human & Machine Intelligence (HMI) at the Frankfurt School of Finance & Management and part of the project group Regulatory Theories of Artificial Intelligence of the Centre Responsible Digitality (ZEVEDI), aims to bring together researchers from ethics, political philosophy, and philosophy of science to discuss questions surrounding the governance of data science. The conference’s guiding question is how to conduct, design and regulate data science for the public good. The proper guidance of data science requires the formulation of both moral and legal norms and frameworks, which may guide individuals, private corporations, as well as public and political institutions. The conference will approach this topic by looking at a range of different domains, exploring the specific challenges they raise and how they might call for different, custom-made solutions. These domains include, but are not limited to, healthcare, scientific research, transportation, surveillance, social media, and law and law enforcement. In these domains, good data science governance is a matter of understanding and balancing a variety of often competing values and ideals, among which are responsibility, trust, fairness, accountability, privacy, cost-efficiency, as well as epistemic goods such as knowledge, understanding and predictive power.

Conference Programme

Friday, 10 June 2022
Room: S3.02, 3rd floor and online

10.00  -  10.30  

Welcome

10.30 - 12.00 Boris Babic (University of Toronto):
The Explainability Bait and Switch
12.00 - 13.30

Lunch break

13.30 - 15.00 Cassandra Grützner (University of Halle-Wittenberg), Maximilian Goldmann (unaffiliated), Moritz Appels (Mannheim University), Rebecca C. Rühle (VU Amsterdam):

Behavioural data governance - the feasibility of data leak prevention through nudging

15:00 - 15.15

Coffee break
15.15 - 16.45 Oliver Buchholz (Tübingen University):
Building Effective Guidelines for Machine Learning: Lessons from Means-End Epistemology
16.45 - 17.00 Coffee break
17.00 - 18.30 Sabina Leonelli (University of Exeter):
Governing Data Circulation: An Underrated Concern for Scientific Inquiry
19:00 Conference Dinner
Saturday, 11 June 2022
Room: S3.02, 3rd floor
 

and online
9.00 – 10.30 Kate Vredenburgh (London School of Economics): Justified Algorithmic Decisions: Thresholds and Randomness
10.30 - 10.45

Coffee break

10.45 - 12.15 Michael Da Silva (University of Ottawa/University of Southampton):
Explainability, Public Reason, and Medical Artificial Intelligence
12.15 - 13.45

Lunch break

13.45 - 15.15 Mahdi Khalili (VU Amsterdam):
Understandable Artificial Intelligence and Virtuous Characters
15:15 – 15:30 Coffee break
15.30 - 17.00 David Danks (University of California, San Diego): Governing Ethical Bias, not Statistical Bias
Wrapping up