Learn why explainability is a critical dimension of responsible AI when governments use machine learning models to determine eligibility for programs and services. Understand how explainability supports human rights, fairness and accountability.
Table of Contents
Question
A government is using a machine learning (ML) model to identify people who qualify for government programs and services. Officials are concerned about the impact on human rights and need a method for determining how the system performed its analysis.
Which core dimension of responsible AI should they consider?
A. Fairness
B. Governance
C. Robustness
D. Explainability
Answer
D. Explainability
Explanation
Explainability empowers users to verify system functionality, check for unwanted biases, increase useful human control, and place appropriate trust in AI systems. This dimension of AI promotes the responsible development and deployment of AI technology for the benefit of society. Without explainability, AI could lose public trust due to inscrutable failures.
Explainability is the core dimension of responsible AI that the government officials should prioritize in this scenario. Explainability refers to the ability to describe how an AI system makes decisions and predictions in a way that can be understood by humans. It involves providing clear insights into the data, algorithms and decision-making processes used by the AI system.
In the context of a government using a machine learning model to determine eligibility for programs and services, explainability is crucial for several reasons:
- Human rights impact: Decisions about access to government benefits and services can have a significant impact on people’s fundamental human rights, such as the rights to food, housing, health, and social security. Without explainability, it may be difficult to identify whether the ML model’s decisions are violating or undermining these rights.
- Accountability and transparency: Government officials have a responsibility to ensure that their decision-making processes are transparent and can be held accountable. If the ML model’s decision-making logic is a “black box,” it becomes challenging to audit the system, identify errors or biases, and hold the government accountable for unfair or discriminatory outcomes.
- Fairness and non-discrimination: Explainability can help detect if the ML model is perpetuating or amplifying societal biases and discrimination. By understanding how the model uses certain features or variables to make predictions, officials can assess whether the model treats different groups fairly and equitably.
- Recourse and appeals: When people are denied access to programs or services, they should have the right to understand why and to challenge the decision if they believe it is wrong. Explainability enables the government to provide meaningful explanations to affected individuals and allows for effective appeals processes.
While other dimensions of responsible AI, such as fairness, governance, and robustness, are also important, explainability is the most directly relevant to the government’s concern about human rights impacts and the need to understand how the ML system performs its analysis. By prioritizing explainability, the government can take steps to ensure that its use of AI is transparent, accountable, and respects the rights and dignities of all people.
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