From What to How. An Overview of AI Ethics Tools, Methods and Research to Translate Principles into Practices <s> An individual focus <s> This theme issue has the founding ambition of landscaping data ethics as a new branch of ethics that studies and evaluates moral problems related to data (including generation, recording, curation,... <s> BIB001 </s> From What to How. An Overview of AI Ethics Tools, Methods and Research to Translate Principles into Practices <s> An individual focus <s> With the increased use of machine learning in decision-making scenarios, there has been a growing interest in creating human-interpretable machine learning models. While many such models have been proposed, there have been relatively few experimental studies of whether these models achieve their intended effects, such as encouraging people to follow the model's predictions when the model is correct and to deviate when it makes a mistake. We present a series of randomized, pre-registered experiments comprising 3,800 participants in which people were shown functionally identical models that varied only in two factors thought to influence interpretability: the number of input features and the model transparency (clear or black-box). Predictably, participants who were shown a clear model with a small number of features were better able to simulate the model's predictions. However, contrary to what one might expect when manipulating interpretability, we found no improvements in the degree to which participants followed the model's predictions when it was beneficial to do so. Even more surprisingly, increased transparency hampered people's ability to detect when the model makes a sizable mistake and correct for it, seemingly due to information overload. These counterintuitive results suggest that decision scientists creating interpretable models should harbor a healthy skepticism of their intuitions and empirically verify that interpretable models achieve their intended effects. <s> BIB002
Few of the available tools surveyed provide meaningful ways to assess, and respond to, the impact that the data-processing involved in their ML algorithm has on an individual, and even less on the impact on society as a whole (Poursabzi-Sangdeh, Goldstein, Hofman, BIB002 . This is evident from the very sparsely populated 'deployment' column of the typology. Its emptiness implies that the need for pro-ethically designed human-computer interaction (at an individual level) or networks of ML systems (at a group level) has been paid little heed. This is most likely because it is very difficult to translate complex human behaviour into simple to use, generalisable design tools. This might not seem particularly important, but the impact this has on the overall acceptance of AI in society could be significant. For example, it is unlikely that counterfactual This potential threat to trust is further heightened by the fact that the lack of attention paid to impact means that ML developers are currently hampered in their ability to develop systems that promote user's (individual or group's) autonomy. For example, currently there is an assumption that prediction = decision, and little research has been done (in the context of ML) on how people translate predictions into actionable decisions. As such, tools that, for example, help developers pro-ethically design solutions that do not overly restrict the user's options in acting on this prediction (i.e. tools that promote the user's autonomy) are in short supply . If users feel as though their decisions are being too curtailed and controlled by systems that they do not understand, it is very unlikely that these systems will meet the condition of social acceptability, never mind the condition of social preferability which should be the aim for truly ethically designed ML (Luciano BIB001 .