The field of AI decision-making systems is moving towards a greater emphasis on fairness and autonomy. Researchers are developing new frameworks and methods to measure and mitigate discrimination in non-binary treatment decisions, and to respect the autonomy of individuals in decision-making processes. The concept of socio-economic parity is also gaining attention, with proposals for novel fairness notions that incorporate socio-economic status and promote positive actions for underprivileged groups. Furthermore, there is a growing interest in exploring the potential of AI to upgrade democratic institutions and provide a forum for participation in decision-making. Noteworthy papers include: A Causal Framework to Measure and Mitigate Non-binary Treatment Discrimination, which proposes a causal framework to extend fairness analyses and mitigate the impact of past unfair treatment decisions. Are clinicians ethically obligated to disclose their use of medical machine learning systems to patients, which argues that mandating disclosure may risk harming patients by providing stakeholders with a way to avoid accountability for harm. The Amenability Framework, which investigates when predictive models can effectively rank individuals by their intervention effects, particularly when direct effect estimation is infeasible or unreliable.