Ethical AI: Ethical implications of AI applications

With AI's rapid involvement across industries, it becomes critical to ensure that AI is practiced ethically. AI practices are diverse, and as such, it is difficult to establish a single framework or protocol to ensure the ethical use of AI. Fair usage of AI is non-negotiable with AI's growing involvement in sensitive applications.

Let us discuss how these sophisticated AI applications face ethical challenges and what enterprises can do to overcome them proactively.

Avoiding biases or discrimination surrounding AI

As per a survey, 68% of respondents believed that ethical principles will not be applied in AI applications by 2030. This indicates that there is significant distrust around using AI ethically. Among these challenges, biases and discrimination are the most pressing concerns.

AI language models are fed and trained on the data. In case the data is from an unauthorized source, then it could be low quality or unfiltered, and might be prone to some biases. AI applications fed on biased data would inherit the same patterns and gaps, leading to biases and discrimination.

Biases and discrimination can have severe issues in unsupervised applications of AI. Here are some of the real-world examples of biases in AI applications:

• A face recognition solution built by feeding images of males of a particular color might not be accurate in processing pictures of people of other colors.

• In the judiciary system, AI can help predict recidivism. Over there, AI algorithms might develop biases from existing data that can lead to discrimination. Such as suggesting that one person might be more likely to commit a criminal offense than another based on available records.

• Another possibility is that AI screening can lead to discrimination in job applications, where applicants with a particular name or demographic might be preferred over other candidates with similar qualifications.

There are several ways to overcome bias in AI applications, including:

• Adequate and diverse data:

It is crucial that an AI model be trained on a diverse set of data that is inclusive and diverse in nature. The data shall represent the entire population equally to mitigate any probability of bias.

• Auditing AI solutions:

It’s crucial to evaluate ML models over time to verify that the model has not developed any bias. A bias detection mechanism that can audit the AI application for diverse use can verify that the system is unbiased.

• Explainable AI:

Implementing AI models that are transparent and explainable can help mitigate any discrimination. Explainability ensures that the reasoning behind AI outputs is unbiased.

AI applications & privacy concerns 

AI solutions are built upon a vast amount of data, often collected from users and their personal patterns and preferences. This brings a serious concern about the ethical use and secure handling of that data. 

Real-world privacy concerns with AI applications:

• Healthcare applications using AI require collecting patient data and sensitive personal information. It is critical to protect the privacy of that data without compromising its security.

• Search engine knowledge graphs must ensure that a user’s individual search history is not reflected on another user within the same demographic.

However, protecting privacy is well within the control of AI applications:

• Minimizing data collection:

The simplest way to protect privacy is to collect the minimum amount of data that is necessary for AI applications to function.

• Data anonymization:

If collecting the data is a must, AI can at least remove any correlation with personal identities through anonymization, which helps protect users' identities.

• Stimulating sample data:

AI can create synthetic data based on real data to ensure that the actual data is not processed or stored. Rather, only similar and stimulated data is utilized to train AI solutions. The SynthVAE model can help generate such a vast amount of synthetic data with AI.

Lack of accountability for unethical AI applications

Who is accountable when an AI model leads to catastrophic loss of personal data or leads to discrimination against a particular race? Even for regulatory and legal bodies, it is complex to hold someone accountable for AI’s unethical outcomes.

Let’s consider a real-world scenario:

• If an autonomous car causes an unfortunate road accident, who is to blame? Would it be the AI engineer, the vehicle manufacturer, or the vehicle owner?

This dilemma surrounding liabilities related to AI incidents is a challenge in perfecting the ethical applications of AI.

To address the challenge, companies can define clear accountability, and regulatory bodies must implement clear guidelines. This could be the only way to implement accountability and ensure that organizations use AI ethically.

Several critical ethical concerns of AI applications

There are some more ethical concerns on the application side. Here are the key issues to be aware of:

• Exploiting IP rights: IP rights define ownership rights for companies. However, AI can often treat publicly available knowledge as free-to-use property. This could compromise IP rights and lead to legal consequences.

• Deep Fakes: AI fakes are the most painful and popular unethical application of AI. AI can create real-like fake content through images, videos, and audio, which can cause chaos and distrust. AI can generate any kind of content and associate it with any public figure.

• Misinformation: AI Hallucinations and errors can lead to false conclusions and incorrect outcomes. Such misinformation can lead users to consequences.

Conclusion

Making AI applications ethical is still a work in progress. It is a complex task to put in a framework or guidelines. Addressing such ethical challenges of AI applications would require significant attention, mitigating potential risks, and putting the right auditing process in place.

Addressing ethical AI concerns can set the pace for AI applications in sensitive industries such as healthcare, law, fitness, recruitment, and others. Ethical AI is important for the future of AI applications that would be diverse, scalable, and inclusive for all users.