Counterfactual Explanations without Opening the Black Box: Automated Decisions and the GDPR (1711.00399v3)

Abstract: There has been much discussion of the right to explanation in the EU General Data Protection Regulation, and its existence, merits, and disadvantages. Implementing a right to explanation that opens the black box of algorithmic decision-making faces major legal and technical barriers. Explaining the functionality of complex algorithmic decision-making systems and their rationale in specific cases is a technically challenging problem. Some explanations may offer little meaningful information to data subjects, raising questions around their value. Explanations of automated decisions need not hinge on the general public understanding how algorithmic systems function. Even though such interpretability is of great importance and should be pursued, explanations can, in principle, be offered without opening the black box. Looking at explanations as a means to help a data subject act rather than merely understand, one could gauge the scope and content of explanations according to the specific goal or action they are intended to support. From the perspective of individuals affected by automated decision-making, we propose three aims for explanations: (1) to inform and help the individual understand why a particular decision was reached, (2) to provide grounds to contest the decision if the outcome is undesired, and (3) to understand what would need to change in order to receive a desired result in the future, based on the current decision-making model. We assess how each of these goals finds support in the GDPR. We suggest data controllers should offer a particular type of explanation, unconditional counterfactual explanations, to support these three aims. These counterfactual explanations describe the smallest change to the world that can be made to obtain a desirable outcome, or to arrive at the closest possible world, without needing to explain the internal logic of the system.

Counterfactual Explanations Without Opening the Black Box: Automated Decisions and the GDPR

The paper "Counterfactual Explanations Without Opening the Black Box: Automated Decisions and the GDPR" by Sandra Wachter, Brent Mittelstadt, and Chris Russell addresses critical concerns in the nexus between algorithmic decision-making and the legal framework established by the European Union General Data Protection Regulation (GDPR). This work is seminal in proposing counterfactual explanations as a viable method for rendering automated decisions transparent and contestable without the necessity to unveil the complex inner workings of machine learning models.

Core Propositions

The authors identify four substantial barriers to implementing a legally binding right to explanation under GDPR:

1. Absence of a legally binding right to explanation in GDPR.
2. Limited applicability of such a right, even if it existed.
3. The technical complexity of explaining algorithmic decision-making.
4. Commercial and privacy constraints against fully disclosing algorithmic details.

To overcome these barriers, the authors propose "unconditional counterfactual explanations" that do not require understanding the internal logic of the decision-making systems but instead provide actionable insights based on external factors. A counterfactual explanation conveys what alterations to the input variables could lead to a different decision outcome, offering a straightforward narrative for the data subject to grasp without technical jargon.

Generating Counterfactuals

The paper illustrates methodologies for generating counterfactual explanations. The process involves creating a modified data point $x'$ close to the original data point $x$ such that the new data point leads to a different decision outcome. Various distance metrics are employed to ensure the counterfactuals are both meaningful and sparse, making them easier to interpret. For instance, the L1 norm weighted by inverse median absolute deviation is suggested for providing human-understandable and minimal explanations.

The authors provide examples using datasets like the LSAT and Pima Diabetes datasets. These datasets demonstrate the feasibility and utility of counterfactual explanations. The results show how changing specific variables, such as LSAT scores or insulin levels, could alter decision outcomes, thus providing clear, actionable insights without exploring the algorithm's internal mechanisms.

Advantages and Implications

The adoption of counterfactual explanations carries significant advantages:

• Regulatory Compliance: Counterfactuals align with GDPR requirements for providing meaningful information without exposing the intricate details of the algorithm.
• User Comprehension: They offer a layman-friendly approach to understanding algorithmic decisions, enhancing trust and acceptance.
• Practical Utility: Counterfactuals can help individuals understand why a decision was made, provide grounds for contesting decisions, and suggest how future decisions might be altered favorably.

Counterfactuals vs. Traditional Explanations

Compared to traditional methods that aim to elucidate the internal state or logic of algorithms, counterfactuals focus on the "external facts" that influence decisions. This distinction is crucial since explaining the millions of variables and dependencies within a modern machine learning model is often infeasible and of limited practical use to non-experts. Counterfactual explanations, therefore, present a practical alternative that is computationally efficient and legally defensible.

Legal and Ethical Considerations

The GDPR, as it stands, supports a limited scope of explanation. Articles 13–15 of the GDPR mandate providing broad overviews of automated decision-making processes to the data subjects, focusing on transparency and accountability. However, these provisions do not necessitate detailed, technical explanations of the algorithms. This paper advocates for counterfactual explanations as a means to surpass the regulatory thresholds set by the GDPR, offering a more granular understanding of decisions and enhancing the ability to contest them effectively.

Future Developments

This research opens avenues for future exploration in AI transparency:

1. Standardization of Metrics: Determining standard metrics for evaluating and presenting counterfactuals across different contexts.
2. Automated Implementation: Developing APIs and automated systems for generating and delivering counterfactual explanations in real-time.
3. Legal Integration: Gauging the acceptability and incorporation of counterfactual explanations within various legal frameworks beyond the GDPR.

Conclusion

Counterfactual explanations provide a robust, minimally invasive approach to enhancing the transparency and contestability of automated decisions under GDPR. These explanations offer clear advantages in terms of regulatory compliance, user comprehensibility, and practical utility. By focusing on the external factors influencing decisions, they avoid the significant pitfalls associated with trying to interpret and explain the internal workings of complex machine learning models. As AI systems become increasingly pervasive, counterfactual explanations represent a critical tool in bridging the gap between technical opacity and the legal and ethical demand for transparency.