How is AI TRiSM helping in eliminating AI trust issues By Janifha Evangeline

How is AI TRiSM helping in eliminating AI trust issues

Janifha Evangeline | Monday, 23 January 2023, 03:19 IST

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The world of AI is growing rapidly since several people have begun to realize how powerful of a tool it can be. Furthermore, owing to the technology expansion, professions, use cases and businesses are also emerging alongside it. In an attempt of rendering a better understanding of the emerging AI ecosystem, Gartner recently organized emerging AI related technologies in AI TRiSM.

In the words of Gartner, AI TRiSM “ensures AI model governance, trustworthiness, fairness, reliability, efficacy, security and data protection. This includes solutions and techniques for model interpretability and explainability, AI data protection, model operations, and adversarial attack resistance.

How is AI TRiSM solving countless problems today

AI - a powerful and diverse tool helps in solving countless problems in the world today. The answer to recommended songs on Spotify is performed by AI, similarly the routes recommended on google maps is AI again. Fraud Analysis, billions in trade securities, self-driving cars, accepting or rejecting loan applications and job applications are all carried out through AI.

While clearly powerful, one has to realize that the advantage a tool offers from its success is also most often comparable to the impact of its failure. In the case of Artificial Intelligence, failure would mean either millions in reputational, legal or even financial losses.

The difficulties of implementing AI TRiSM in an organization

Enterprises that looking forward to implement Responsible AI should be focusing more on translating the important ethical principles into quantifiable metrics which can be used in daily operations & this calls for making the technical, organizational, operational as well as reputational considerations.

Technical difficulties of implementing AI TRiSM

The effectiveness of a Responsible AI framework cannot be easily measured by using the tried & true business metrics such as either the website traffic or click-through rates. Now, new technical metrics should be developed in order to monitor factors that are related to AI trust, AI risk as well as AI security. And, without good metrics & methods, enterprises will be finding it more difficult to effectively maintain their Responsible AI framework. Also, they will find it difficult to conduct essential decision-making & build consensus around AI initiatives. Although, there are promising signs, there are counterfactual analysis & metrics like error rates making it easier for firms to implement a Responsible AI framework.

Operational Difficulties of implementing AI TRiSM

Enterprises leveraging and implementing AI technologies should hold governance structures in place for addressing accountability, conflict resolution as well as competing incentives. These structures should be transparent as well as focused on addressing any misalignment, bureaucratic issues, & lack of clarity about AI-related operations.

Top three strategies to achieve AI TRiSM

Guided documentation: Like how Artificial Intelligence grows in complexity, so do the external pressures & expectations grow. Internal risks & external legal guidelines render a long list of variables which should be managed & tested against that are leading to long & tedious documentation processes. This is highly essential but far too difficult step in the risk management process. Information should travel via long chains of staff & somewhere along the line it is often found that information is either missing or incomplete. This leads to the cycle to repeat itself from the start. It is no surprise that with such huge quantities of data things go wrong, however, these errors can be minimized.

One such company that provides guided documentation via guided checklists, document templates as well as on automated report builder which pulls test results & formats them correctly within documentation is FAIRLY. Artifacts from the codebase are first analyzed & a flag is thrown if any of these artifacts are missing within documentation. By ensuring that the report building is consistent & intuitive, a lot of time is freed for developers for developing & strengthening the model.

Automated risks and bias checks: Bias occur when patterns are found where they are not supposed to be found, and most often owing to an insufficient data set. When such an issue occurs, the Artificial Intelligence model may start making decisions that are based on unwanted parameters such as Name length, race, gender etc. When decisions are made based on these arbitrary parameters, models become prone to error. And far worse, they would even become prone to discrimination. Therefore, this is why it is highly crucial for monitoring AI Bias & risk to catch these errors even before they are too deeply ingrained in the behavior of a model. FAIRLY provides both behavior as well as bias checks within its platform which can be used with the check of a button. This makes risk analysis easier than ever before. Furthermore, these features get into the automated report builder and allows users to upload results into the API or directly into documentation.

Transparency: One of the biggest issues at present is a lack of trust in Artificial Intelligence models, that is stemming from a lack of understanding. Several clients today are not comfortable interacting with machines like the other people they are used to. This issue is looked into when the decision making inside the black box is either not easy or impossible to explain and this leaves the clients without answers or comfort. Therefore, by rendering audit & comment trails, a timeline of discussions as well as decisions that are centered around the model can be seen, and this allows for a thorough as well as clear understanding which can then be transferred from knowledgeable staff to interested consumers.

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