The reason AI is considered to have broken all barriers concerning information retrieval also conceals a major snag. Although generative AI replies to queries objectively, the data it processes might be corrupted and biased, giving rise to manipulated data in the query outcome. This issue is quite a serious one because misinformation can be dangerous to a society that is often polarised based on differences. Thus, it is an ethical responsibility to make sure that any bias in AI behaviour is immediately addressed.
The best way to deal with this problem systematically is by designing an ethical framework that would automatically filter out misinformation. Modern fact-checking tools deploy a technology similar to this and are highly effective in detecting fake news. In this article, let us identify the major ethical risks that are associated with AI, the evaluation of AI performance, the risk assessment process, and how to deal with such risks successfully.
Identifying the risks
Before one begins strategising against probable ethical concerns, one must first internalise the ways to identify the issues and where to look for them.
- Privacy standards: Any AI enterprise must adhere to privacy standards and integrate regulatory mechanisms into all its functioning. To ensure transparency, it must also disclose its privacy policy to the users and disclose how the data that is collected from them is used.
- Accountability and security: Without accountability, no serious measure can be implemented with the least degree of efficacy. Thus, investigative designs must be in place so that measures can be taken in case of a data breach.
- Bias: Inherent bias in data that is fed to the AI during its learning process is hard to weed out. However, it is the responsibility of the AI tech developers to ensure that the results produced by the AI model do not have any gender, race, socioeconomic or religious bias. Therefore, a system must be there in place that trains the AI model to identify the presence of bias in the database and how to deal with it.
Evaluation of performance
Since AI deals with data, it is important to realise the importance of data neutrality. There are specific ways to instate accountability, and some of them are:
- Data source evaluation: Since AI consumes a lot of data to work its logic on and yield results, it is not possible to fact-check everything manually. Therefore, AI must be trained to identify corrupted data so that it can eliminate the same.
- Evaluating AI performance: It is important to evaluate how the AI model is performing or responding to the systems that are in place to train it. Since bias removal is an important metric in performance evaluation, it must be understood how well the AI model has incorporated the ethos of diversity, equality and representation.
- Explainability: The AI model must be tested for the accuracy of its logical reasoning. Thus, if it has churned out an output, it should be able to explain how it has come to a conclusion while including certain information and discarding others. These responses are exceedingly important because they give the developers an idea about how the machine learning process is shaping up.
Risk Assessment
One of the best ways to understand the degree of risk posed by the AI model through its responses is to approach the stakeholders directly. These are the steps:
- Interviewing diverse people: Talking to various ethnic groups about their experience with a particular AI model can reveal data about the stage in which the AI model is based. If inherent biases are discovered at this phase, they could be the basis for an important step forward in dealing with them.
- Clear communication: It is important to clarify the strengths and weaknesses of the AI model they are using to the stakeholders. Doing this will instantly set the expectation levels and reduce the risks of misunderstandings.
Dealing with risks
After the assessment process is done, it is important to address the issues found in that stage:
- Augmentation: Data augmentation is one of the best possible ways to reduce the risk of bias within the AI machine learning process. By feeding data that is sensitive to representational diversity and equality, the AI model can be made to deliver better results.
- Human intervention: A process should be in place that ensures that humans oversee AI critical thinking processes so that snags can be detected at a fundamental level.
- Algorithmic adjustments: The coding involved in the training process can be tweaked from time to time to ensure the removal of biases. Thus, AI models can be made increasingly trustworthy with financial institutions like banks and NBFCs, who would then utilize them for their internal processes.
Thus, it can be understood that there are various complex aspects of launching an AI in the online marketplace. It comes with a lot of responsibility and accountability that should not be dodged; otherwise, the harm inflicted on the society would be unquantifiable.
