Technological unemployment and human disenhancement. - Michele Loi (Ethics Inf. Technol., 2015)

A Moral Framework for Understanding Fair ML through Economic Models of Equality of Opportunity. - Hoda Heidari; Michele Loi; Krishna P. Gummadi; Andreas Krause 001 (FAT, 2019)

Towards an Ethical Code for Data-Based Business. - Michele Loi; Christoph Heitz; Andrea Ferrario; Anita Schmid; Markus Christen (SDS, 2019)

Cybersecurity in health - disentangling value tensions. - Michele Loi; Markus Christen; Nadine Kleine; Karsten Weber (J. Inf. Commun. Ethics Soc., 2019)

The societal and ethical relevance of computational creativity. - Michele Loi; Eleonora Viganò; Lonneke van der Plas (ICCC, 2020)

A Comparative Assessment and Synthesis of Twenty Ethics Codes on AI and Big Data. - Michele Loi; Christoph Heitz; Markus Christen (SDS, 2020)

A blindspot of AI ethics: anti-fragility in statistical prediction. - Michele Loi; Lonneke van der Plas (CoRR, 2020)

A Series of Unfortunate Counterfactual Events: the Role of Time in Counterfactual Explanations. - Andrea Ferrario; Michele Loi (CoRR, 2020)

Fair Equality of Chances for Prediction-based Decisions. - Michele Loi; Anders Herlitz; Hoda Heidari (AIES, 2021)

Towards Accountability in the Use of Artificial Intelligence for Public Administrations. - Michele Loi; Matthias Spielkamp (AIES, 2021)

On the Moral Justification of Statistical Parity. - Corinna Hertweck; Christoph Heitz; Michele Loi (FAccT, 2021)

Transparency as design publicity: explaining and justifying inscrutable algorithms. - Michele Loi; Andrea Ferrario; Eleonora Viganò (Ethics Inf. Technol., 2021)

How Explainability Contributes to Trust in AI. - Andrea Ferrario; Michele Loi (FAccT, 2022)

Is calibration a fairness requirement?: An argument from the point of view of moral philosophy and decision theory. - Michele Loi; Christoph Heitz (FAccT, 2022)

People are not coins: Morally distinct types of predictions necessitate different fairness constraints. - Eleonora Viganò; Corinna Hertweck; Christoph Heitz; Michele Loi (FAccT, 2022)

The Robustness of Counterfactual Explanations Over Time. - Andrea Ferrario; Michele Loi (IEEE Access, 2022)

Group Fairness Is Not Derivable From Justice: a Mathematical Proof. - Nicolò Cangiotti; Michele Loi (CoRR, 2022)

A Justice-Based Framework for the Analysis of Algorithmic Fairness-Utility Trade-Offs. - Corinna Hertweck; Joachim Baumann 002; Michele Loi; Eleonora Viganò; Christoph Heitz (CoRR, 2022)

Distributive Justice as the Foundational Premise of Fair ML: Unification, Extension, and Interpretation of Group Fairness Metrics. - Joachim Baumann 002; Corinna Hertweck; Michele Loi; Christoph Heitz (CoRR, 2022)

Unification, Extension, and Interpretation of Group Fairness Metrics for ML-Based Decision-Making. - Joachim Baumann 002; Corinna Hertweck; Michele Loi; Christoph Heitz (EWAF, 2023)

Classification Parity, Causal Equal Protection and Algorithmic Fairness. - Marcello Di Bello; Nicolò Cangiotti; Michele Loi (EWAF, 2023)

FairnessLab: A Consequence-Sensitive Bias Audit and Mitigation Toolkit. - Corinna Hertweck; Joachim Baumann 002; Michele Loi; Christoph Heitz (EWAF, 2023)

What's Distributive Justice Got to Do with It? Rethinking Algorithmic Fairness from a Perspective of Approximate Justice. - Corinna Hertweck; Christoph Heitz; Michele Loi (AIES (1), 2024)

Group Fairness Refocused: Assessing the Social Impact of ML Systems. - Corinna Hertweck; Michele Loi; Christoph Heitz (SDS, 2024)

Discrimination for the sake of fairness by design and its legal framework. - Holly Hoch; Corinna Hertweck; Michele Loi; Aurelia Tamò-Larrieux (Comput. Law Secur. Rev., 2024)

Causal Equal Protection as Algorithmic Fairness. - Marcello Di Bello; Nicolò Cangiotti; Michele Loi (CoRR, 2024)

The representative individuals approach to fair machine learning. - Clinton Castro; Michele Loi (AI Ethics, 2025)