Introduction
In recent years, Artificial Intelligence (AI) has taken over every domain, and the boom in AI and its adoption has been quite significant. Over 70 percent of organizations have already adopted AI in one or more parallels of their business, and the rest are due to join in. With this rise in AI adoption, and the increasing use of AI in life-altering domains like healthcare, banking, and insurance, the concern for an ethical and fair AI is much more critical now.
There is no doubt that AI is the future, but as the popular statement goes, “With great power comes great responsibility”, we too must realize that ethical responsibility and ensure fair and Responsible AI (RAI) is delivered.
Why is RAI required?
The primary goal of RAI is to build and deploy AI solutions that adhere to widely accepted standards of morals and ethics and provide fair decisions towards all unprivileged/marginalized groups without any prejudices or bias.
AI can have biased decision-making due to any unchecked biases present in the input data, which could lead to discrimination towards certain groups. These unethical biases are most frequently introduced through gender, race, age, and location-related information. For example, in Amazon, the AI setup for hiring was preferring male employees for tech roles. Another one is from the popular Natural Language Processing (NLP) algorithm, Word embedding, that categorized Caucasian and European names as pleasant and African American names as unpleasant.
These examples shed some light on how the inherent bias in our society is captured and makes it through the biased data, into our AI solutions, making it unethical. The consequences of unfair AI could be quite drastic, which may include monetary loss, compliance issues, reputational damage, and even criminal lawsuits. Implementing RAI could not only save an organization from these undesired outcomes but would also lower trust in AI, among stakeholders, developers, and consumers.
Benefits of RAI
Introducing an RAI framework into all AI solutions in production will become crucial for organizations employing AI and is the next step towards ensuring AI Trust. RAI can provide the following benefits:
- Minimize unwanted bias:
Ensure the underlying data and algorithms are as fair and unbiased as possible by incorporating bias mitigating steps early on. - Compliance monitoring:
Continuous monitoring of the solution, for compliance with the various policies and laws regarding fair treatment of all individuals. - Ensure AI transparency:
Enable trust in AI through transparency and interpretability of the underlying decision-making process, via Explainable AI. - Benefit clients and markets:
Mitigate risk and establish systems that benefit stakeholders, clients, and even society at large. - Protect the privacy and security of data:
Prioritize security and privacy of data in order to safeguard private/sensitive information and ensure it’s never used unethically.
Steps to take to enable AI Fairness
Increasing trust in AI is a gradual and slow process, but these are some steps that can be taken toward enabling fair and ethical AI:
- Principles and governance:
In order to build and sustain trust and confidence in AI and AI-based technologies, it’s essential to establish a Responsible AI mission (and principles), which is a first step toward setting up your transparent governance structure. - Risk, policy, and control:
Structure policies to help in mitigating risk towards compliance issues, and utilize a proper risk management framework for continuous monitoring of compliance with all current and any future laws. - Technology and enablers:
Employ tools and techniques that enable and promote true AI governance, which would consist of fairness, interpretability, transparency, and traceability, along with performance, privacy, and security. - Culture and training:
Encourage leadership to appoint RAI as one of the critical business requirements, and establish mandatory training programs for all employees and developers in order to educate the mission of RAI, and its importance.
