An overview and recommendations from the U.S. experience
Copyright: brookings.edu – “Six Steps to Responsible AI in the Federal Government”
There is widespread agreement that responsible artificial intelligence requires principles such as fairness, transparency, privacy, human safety, and explainability. Nearly all ethicists and tech policy advocates stress these factors and push for algorithms that are fair, transparent, safe, and understandable.
But it is not always clear how to operationalize these broad principles or how to handle situations where there are conflicts between competing goals. It is not easy to move from the abstract to the concrete in developing algorithms and sometimes a focus on one goal comes at the detriment of alternative objectives.
In the criminal justice area, for example, Richard Berk and colleagues argue that there are many kinds of fairness and it is “impossible to maximize accuracy and fairness at the same time, and impossible simultaneously to satisfy all kinds of fairness.” While sobering, that assessment likely is on the mark and therefore must be part of our thinking on ways to resolve these tensions.
Algorithms also can be problematic because they are sensitive to small data shifts. Ke Yang and colleagues note this reality and say designers need to be careful in system development. Worrying, they point out that “small changes in the input data or in the ranking methodology may lead to drastic changes in the output, making the result uninformative and easy to manipulate.”
In addition, it is hard to improve transparency with digital tools that are inherently complex. Even though the European Union has sought to promote AI transparency, researchers have found limited gains in consumer understanding of algorithms or the factors that guide AI decisionmaking. Even as AI becomes ubiquitous, it remains an indecipherable black box for most individuals.
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In this paper, I discuss ways to operationalize responsible AI in the federal government. I argue there are six steps to responsible implementation:
- Having concrete codes of conduct
- Appropriate operational tools for promoting major ethical principles and fighting bias
- Developing clear evaluation benchmarks and metrics
- Relying upon technical standards to help with common problems
- Experimenting via pilot projects and organizational sandboxes
- Having a mix of technical nontechnical skills in the workforce […]
Read more: www.brookings.edu
An overview and recommendations from the U.S. experience
Copyright: brookings.edu – “Six Steps to Responsible AI in the Federal Government”
But it is not always clear how to operationalize these broad principles or how to handle situations where there are conflicts between competing goals. It is not easy to move from the abstract to the concrete in developing algorithms and sometimes a focus on one goal comes at the detriment of alternative objectives.
In the criminal justice area, for example, Richard Berk and colleagues argue that there are many kinds of fairness and it is “impossible to maximize accuracy and fairness at the same time, and impossible simultaneously to satisfy all kinds of fairness.” While sobering, that assessment likely is on the mark and therefore must be part of our thinking on ways to resolve these tensions.
Algorithms also can be problematic because they are sensitive to small data shifts. Ke Yang and colleagues note this reality and say designers need to be careful in system development. Worrying, they point out that “small changes in the input data or in the ranking methodology may lead to drastic changes in the output, making the result uninformative and easy to manipulate.”
In addition, it is hard to improve transparency with digital tools that are inherently complex. Even though the European Union has sought to promote AI transparency, researchers have found limited gains in consumer understanding of algorithms or the factors that guide AI decisionmaking. Even as AI becomes ubiquitous, it remains an indecipherable black box for most individuals.
Thank you for reading this post, don't forget to subscribe to our AI NAVIGATOR!
In this paper, I discuss ways to operationalize responsible AI in the federal government. I argue there are six steps to responsible implementation:
Read more: www.brookings.edu
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