Thou Shall Judge But With Fairness: Why Ethics Are Important In Machine Learning

Each generation of computing is identified by a revolution. What started with vacuum tubes graduated to transistors, then integrated circuits, and is now powered by graphics cards. Artificial Intelligence (AI) is one of the bases of computing at its peak. Is it not exciting that your job application will be scanned by an AI algorithm and deem whether you're suited for the applied job? What is even more fun is that while you swipe over potential matches on dating apps, an algorithm is silently learning your preferences and building a stronger search.

Welcome to the fifth generation of computing, where AI is one of the major players. It has surely accelerated business and made entertainment more personalized. But it was a matter of time before a powerful technology would show its impact on the social and political front. Take the example of the Facebook-Cambridge Analytica scandal. The instance when personal data and analytical algorithms were misused to sway an important event like the US presidential elections. It gave more teeth to those demanding stronger regulations for AI ethics.

The demand for a ‘Trustworthy AI’ seems justified since the algorithms are pervasive in different social paradigms. The ethical issues in machine learning can influence political scenarios like elections or disrupt the psychological makeup of individuals. Take the example of how the impact of an artificial intelligence chatbot on Brazilian adolescents' body image was reported in the year 2021.

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Ethical Issues in Machine Learning

The ethical implications of AI-ML algorithms can be categorized based on the principles they had affected:

Compromise with human welfare

The ethical compromises caused by algorithms may affect a person's mental and social well-being. The influence of algorithms to sway the rational thinking of humans can be a significant infringement. Take the instance of riots provoked by deliberate acts causing social or political disruptions. Another potential compromise may involve the abuse of consent. Some demographics may not understand consent or their control over it. If an AI-ML algorithm causes misinterpretation of consent among users, this also counts as an ethical issue. 

Moreover, the identity of a person or the community they belong to should not cause unfavorable outcomes from models. This is important to provide fair competition among businesses or candidates. For instance, loan application rejection could be influenced by the political leanings of the applicant.

Compromise with human safety

Is your AI-ML algorithm causing mental, social, or environmental harm among the users? Then the risks should be identified and remedied on priority. Human safety provisions should protect against users’ risks due to hacking or malicious access. An infringement on system robustness may pose risks to the users.

AI-ML models hold the potential for exploitation since what may have started as a positive use may get modified to pursue malicious goals. For instance, compromised AI models that control delivery drones could be used to execute attacks. Additionally, the model should be reliable such that its functions are reproducible in conditions similar to its development and testing. An unreliable system may lead to a loss of trust among the service consumers.

Another expectation from robust systems is the ability to address unpredictable situations. Unknown scenarios may expose the users to risks such as identity exposure or loss of credentials. Therefore, the inability of a system to handle unknown risks might result in ethical compromise.

Compromise with data privacy

The demand for the protection of individual information has grown over the past decade, such that it led to the establishment of regulations like GDPR, HIPAA, and CCPA. These regulations dictate how the data collected from users should be stored and processed by service providers based on the intention. Such provisions, too, place importance on informed consent. Individuals who comprise the collected data must be informed of its intended use, storage, and further sharing approaches.

The Facebook-Cambridge Analytica scandal also fueled concerns about the unethical use of personal data to target demographics and drive recommendations for political and financial gains. Apart from data protection, an organization must also secure the different processes which deal with the collected data. These include pre-processing, versioning, analysis, reuse, and warehousing.

Opaque algorithms

Transparency is another requisite for trust. It touches on the accountability of the AI-ML systems for the decisions taken by them and the logic behind them. Therefore it is a mix of governance as well as explainability.

Now, algorithms cater to different stakeholders. The inability to provide explanations behind decisions, including traceability and low-level or data-specific interpretations, may cause them to become black boxes. Thus causing distrust among the stakeholders.

AI systems that emulate human communication, such as chatbots, must also be transparent about their capabilities and purposes. A trustworthy system would communicate well to the user that they are chatting with a bot. Furthermore, testing the learning mechanisms of such bots can prevent them from learning human biases.

Biased algorithms

This aspect of ethical issues in AI-ML systems ensures that individuals are treated equally while making decisions. While deeply rooted in philosophy, fair algorithms must consider demographic factors like race, gender, socio-economic background, or political leaning. 

Among other ethical implications of bias, the algorithms must not give preferential or discriminatory treatment to a specific group of people. Such issues came to the fore when imbalanced datasets and human biases crept into critical domains such as healthcare and judicial organizations. However, ethical issues are not just limited to bias but also include the possibility that a specific group of people may not be able to access the systems’ services. For instance, persons with disabilities or linguistic constraints should not face difficulty using AI-ML services.

The unaccountability of Impact

Accountability is another ethical requirement that stems from governance. A trustworthy AI-ML system should be able to explain how the decision was made and how the impact of the decisions was measured. Apart from gauging any possible harm, rollbacks and corrections should be provided in case of an infringement. 

The human-in-the-loop also holds accountability since oversight during development can lead to the issues mentioned above. This is more important since current systems are mostly semi-supervised, which means a person reviews the decisions taken by the system. For instance, a credibility calculator for a loan application portal.

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How is Trustworthy AI Good for Your Business

A recent scorecard published by Ranking Digital Rights (RDR), an independent research program put numbers on the trust placed in big tech companies. The scoring measured trust in algorithm-driven data curation practices and showed how dismal the current state is.

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The major players shown in the sentiment scorecard drive the public acceptance of products and service providers. Moreover, AI and business have developed a symbiotic relationship. While businesses have accepted that AI will improve work processes and expected returns, they also found it worthwhile to invest in its research. One of the biggest pieces of proof is the presence of the big tech companies at the top conferences.  Among the values encoded in machine learning research published in 2021, the participation of these companies in research has grown over the past decade. It is therefore paramount to gain the trust of stakeholders in AI-ML applications to promote acceptance among the masses.

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A few benefits your business will experience when based on trustworthy AI are

• Better participation from users: There is a strong correlation between data ethics and user participation. The users will be willing to share their data truthfully if they believe that it will be protected from unethical use.
• Improved accessibility and reliability of your product: The acknowledgment of risks and drafting procedures to handle them will result in a more accessible and reliable product. On one hand, your product would cater to wider demography and build a larger consumer base. On the other, reliable functions would minimize exploitation of any surprise scenarios by an adversary.
• Your business will promote a healthier economy: Socio-economic and political impacts are a recurrent theme in unethical AI debates. Sticking to ethical practices will keep your business away from legal soups and litigations. In addition, stable businesses promote a thriving economic environment.
• Your business will promote conscious computing: Ethics is not limited to humans. AI algorithms that tend to the natural environment can attract monetary incentives like carbon points.

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How to Achieve Trustworthy AI

Now that we have talked about artificial intelligence and machine learning ethical concerns in business, it is time to guide you towards building ethics in machine learning. An ethics-preserving system is a significant step toward using technology for human welfare. Here are some achievable steps that can help your products ensure the same.

• Encourage meaningful participation: This may start at the time of requirements elicitation and design. Getting to know the spread of the expected stakeholders and their concerns will enable a holistic approach. Additionally, organizations with diversity in the workforce and collaborations among different disciplines can help expand product vision.
• Practice data minimization during collection: Please follow the principle of data minimization to ensure privacy compliance when collecting data from the users. This means the collection of data that is sufficient for the purpose and not any more, and clearly communicating to the individuals about the intended use. Also, in some cases, the data should be destroyed once necessary operations have been done.
• Ensure accessibility: The services provided by your system should be inclusive of different demographics. This can be achieved through a user-friendly interface and affordable access mechanisms.
• Regular impact assessments: The technology should be audited periodically to check fairness, transparency, and robustness. Since the hiring of a compliance team may cost more to your business, a well-rounded monitoring solution like the Censius AI Observability platform can cater to these specific requirements.

• Your project can be easily monitored for violations, data-related issues, and get timely alerts for potential drifts.
• Explainability reports generated by the platform provide global and local explanations, as well as cohort analysis for comprehensive transparency.
• The robustness checks will help you achieve a more reliable product. If you are wondering how your model will respond to unexpected inputs, then test it out on the Censius AI observability platform.
• The upcoming feature of what-if analysis would help gauge model behavior for variations in inputs, thereby helping you build a reliable system.

• Audits for the technology: The metrics for fairness including bias measurement should be considered during the design. Additionally, robustness metrics like resilience to hacking, and outputs of explainability reports should drive the development of your product.

If we have stoked your curiosity on how the Censius AI observability platform can initiate your team to ethics and its benefits, you can delve further by getting a customized demo or by signing up for the 14-day free trial.

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