Interpretable to Whom? A Role-based Model for Analyzing Interpretable Machine Learning Systems (1806.07552v1)

Abstract: Several researchers have argued that a machine learning system's interpretability should be defined in relation to a specific agent or task: we should not ask if the system is interpretable, but to whom is it interpretable. We describe a model intended to help answer this question, by identifying different roles that agents can fulfill in relation to the machine learning system. We illustrate the use of our model in a variety of scenarios, exploring how an agent's role influences its goals, and the implications for defining interpretability. Finally, we make suggestions for how our model could be useful to interpretability researchers, system developers, and regulatory bodies auditing machine learning systems.

• The paper proposes a role-based model defining six distinct stakeholder roles to tailor interpretability strategies.
• It highlights how differing roles—from creators to decision-subjects—affect requirements for transparency and decision-making.
• The model offers practical guidance for developers and regulators, positioning interpretability within explicit ethical and performance contexts.

A Role-based Model for Analyzing Machine Learning Interpretability

The paper "Interpretable to Whom? A Role-based Model for Analyzing Interpretable Machine Learning Systems" presents a nuanced model for interpreting machine learning systems by delineating the roles of agents interacting with these systems. Interpretability in machine learning has garnered extensive attention, with debates centering on how to efficiently communicate the decision-making processes of complex models. This paper shifts the focus from generalized interpretability to a more targeted question: "interpretable to whom?"

Framework for Role-Based Interpretability

The authors propose an ecosystem model that identifies six distinct roles agents can play concerning a machine learning system: Creators, Operators, Executors, Decision-subjects, Data-subjects, and Examiners. Each role interacts differently with the machine learning system and each possesses unique goals and beliefs with respect to interpretability.

1. Creators are responsible for constructing the machine learning system, which includes designing, implementing, and optimizing it for various performance metrics.
2. Operators interact directly with the system, usually inputting data and receiving the output. They may require systems to elucidate which inputs were most influential.
3. Executors are decision-makers who leverage the machine learning system's outputs to guide their decision-making processes.
4. Decision-subjects are affected by these decisions and might seek explanations for transparency, contestability, or adaptation purposes.
5. Data-subjects are those whose data are used in training. With increasing data privacy rights, they may demand insight into how their data influenced system outputs.
6. Examiners are auditors or investigators who assess the system's accountability, compliance, and fairness.

Implications for Interpretability

The paper convincingly argues that a one-size-fits-all approach to interpretability is insufficient. Recognizing each role's unique needs and objectives can produce more meaningful interpretability strategies. For instance, while Creators require transparent systems to enhance model performance and innovation, Decision-subjects might prioritize understandable explanations to ensure fairness and contestability.

Practical and Theoretical Contributions

This role-based model offers practical guidance for system developers and regulatory bodies tasked with ensuring adherence to transparency requirements. It equips machine learning systems' creators with a structured approach to assess and tailor interpretability efforts suitable for various stakeholders' requirements.

Theoretically, the paper adds a layer to the discourse on interpretability by suggesting that it must be assessed along the axes of explainability, transparency, and the role’s context. This comprehensive framework is crucial for moving toward a more formalized, scientific understanding of interpretability in machine learning.

Speculation on Future Directions

Future developments in AI could entail increasingly autonomous systems, necessitating the expansion and evolution of the described roles. The line between these roles may blur as artificial agents assume roles traditionally held by humans, such as operators or even executors. As data privacy regulations tighten globally, understanding the role of data-subjects becomes increasingly imperative. Additionally, inter-role dynamics and potential conflicts, such as those between executors and decision-subjects or creators and examiners, will likely become a focal point for resolving systemic biases and ensuring robust ethical frameworks.

In conclusion, this paper provides a structured lens through which machine learning interpretability can be analyzed, emphasizing role-specific considerations and fostering a more tailored, nuanced approach to enhancing and evaluating interpretability in complex machine learning ecosystems.