Algorithmic Accountability
Definition
Risus neque consectetur arcu nunc vulputate amet. Phasellus posuere nulla non feugiat lorem dui mattis eget cras. Nullam ut sed quam ac. Dignissim tempor neque mattis eu nascetur mauris malesuada. Venenatis quis varius lorem amet tristique ultricies adipiscing condimentum nulla. Amet viverra sit magna aliquet donec consectetur nunc aliquet consectetur. Eget id tempor eu eget urna facilisi tortor. Augue arcu lacus tortor purus ut neque risus diam.
Ac tincidunt maecenas dignissim facilisis nec dui velit bibendum. Odio maecenas mattis eget senectus duis eget. Nisl proin vitae quam tempus libero morbi donec mauris. Interdum nec pellentesque consequat auctor. Massa semper orci dictumst consequat nam pretium volutpat senectus. Massa amet tortor amet fermentum libero amet dui at aliquet. Diam placerat sollicitudin gravida nulla sagittis. Urna semper morbi nec in fermentum in massa venenatis. Amet commodo mi lacus fringilla aliquam aenean feugiat. Lacus vel gravida enim arcu laoreet et sit at malesuada. Commodo massa suspendisse pharetra sed sit amet vitae. Viverra ornare facilisis volutpat convallis justo nisl sem risus lectus. Vestibulum senectus eget quam ultrices consequat.
Real-World Example
Take the example of a bank using an AI system to approve or deny loan applications. In the US, when denying a loan, a bank must provide the applicant with an explanation of why the decision was made. The purpose of this explanation is to demonstrate that accurate information was used in a way that conforms with the bank’s decision-making processes. It also gives the applicant an opportunity to correct any inaccurate information or to dispute the rigor of the process.
A loan applicant likely prefers clearly communicated information, like “Your credit score was 20 points short of the automatic approval threshold and additionally, the assets you provided were $8,000 short of securing the loan outright.”Yet, to be able to provide this kind of clear statement, the bank’s data and AI team needs to understand how the data inputs and the models constituting the AI system result in the outputs (AI interpretability). This, in turn, requires an understanding of how the models and datasets were developed and what kinds of governance they were subjected to throughout their lifecycles.
Useful Resources:
- [Useful Resource Name Here] by [Author Name
- [Useful Resource Name Here] by [Author Name]
- [Useful Resource Name Here] by [Author Name]
- [Useful Resource Name Here] by [Author Name]
.svg)
