Unethically Me: Explaining Artificial Intelligence’s Results by Being Unethical

The goal of this workshop is to examine what is needed to explain artificial intelligence (AI) for lay end-users. In a full day workshop we want to engage with an interdisciplinary group of researchers to find best practices in explaining and interpreting results from AI for those with only with limited knowledge of AI.

[1]  Alun D. Preece,et al.  Stakeholders in Explainable AI , 2018, ArXiv.

[2]  Tillman Weyde,et al.  What makes a good explanation? Cognitive dimensions of explaining intelligent machines , 2019, CogSci.

[3]  Luciano Floridi,et al.  From What to How. An Overview of AI Ethics Tools, Methods and Research to Translate Principles into Practices , 2019, ArXiv.

[4]  Alexandra Kirsch,et al.  Explain to whom? Putting the User in the Center of Explainable AI , 2017, CEx@AI*IA.

[5]  Michael Veale,et al.  Enslaving the Algorithm: From a “Right to an Explanation” to a “Right to Better Decisions”? , 2018, IEEE Security & Privacy.

[6]  R. Binns,et al.  Algorithmic Accountability and Public Reason , 2017, Philosophy & Technology.

[7]  Virginia E. Eubanks.  Automating Inequality: How High-Tech Tools Profile, Police, and Punish the Poor , 2018 .

[8]  Olivier Serrat.  The SCAMPER Technique , 2017 .

[9]  E. Moss,et al.  AI’s social sciences deficit , 2019, Nature Machine Intelligence.

[10]  Hannah Lebovits.  Automating Inequality: How High-Tech Tools Profile, Police, and Punish the Poor , 2018, Public Integrity.

[11]  Luciano Floridi,et al.  From What to How: An Initial Review of Publicly Available AI Ethics Tools, Methods and Research to Translate Principles into Practices , 2019, Science and Engineering Ethics.

[12]  Elisabeth André,et al.  "Do you trust me?": Increasing User-Trust by Integrating Virtual Agents in Explainable AI Interaction Design , 2019, IVA.