Wednesday, November 29, 2023

Four Principles and Best Practices of Responsible AI

By Apoorva Kasam - October 05, 2023 4 Mins Read

Four Principles and Best Practices of Responsible AI

While AI solutions offer great benefits, their development is hard to manage. These solutions must be free of bias and discrimination and be able to explain their intentions adequately. Implementing responsible AI (RAI) principles facilitates safe, ethical, and acceptable results.

Why are Responsible AI Principles Valuable to Firms?

As per a recent MIT Sloan Management Review and BCG report, “Building Robust RAI Programs as Third-Party AI Tools Proliferate, ”

Building Robust RAI Programs as Third-Party AI Tools Proliferate

RAI principles can help firms design, develop, and implement AI systems that benefit individuals, society, and firms while reinforcing societal value.

Firms that value RAI principles in their AI governance, policies, and practices can efficiently understand and address the associated risks.

What are the Four Principles of RAI?

1. Fairness

Firms use AI tools in various decision-making processes. Computational and societal bias in data contributes to discrimination in such decisions. Issues like these arise when the algorithms provide systematically biased results due to false assumptions.

But, the major challenge is ensuring that undesired biases are mitigated through relevant interventions, practices, and RAI principles.

2. Privacy

RAI focuses on users’ privacy rights and strives to secure them. AI systems must understand private and public data and its limitations. These systems are connected to the internet and feature state-of-the-art cybersecurity measures like facial and role recognition.

AI systems must handle personal information in compliance with privacy laws and regulations. Firms must implement data governance practices and seek informed consent during data collection.

3. Security

Attackers find new methods to defend the AI system’s security as AI evolves. Hence, it is vital to prevent the AI system from attackers from changing its intended behavior. Also, using AI in particular areas can induce vulnerabilities that can impact public safety.

For example, adversarial attacks can involve data and model poisoning. While data poisoning occurs when hackers inject deceptive data into training data sets, the latter occurs due to model manipulations.

4. Transparency

Transparency highlights the need for visibility across AI systems for the users working with the systems and those impacted. Firms must strive to make AI algorithms, models, and decision-making processes explainable to users and stakeholders. It helps build trust and understand how AI systems drive decisions.

As most AI systems work in a closed environment, there is a need for clarity and transparency. AI systems are trained with ML that often fails to differentiate poor or high-quality data. Hence, training the machine learning model to monitor the incoming data is essential.

What are the Best Practices to Achieve the Principles?

1. How to Achieve Fairness

The first step is to analyze the data the AI learns from. If the data reflects existing undesired biases, the model will learn from them. But, the risks are not limited to the AI model. Firms must develop processes to determine undesirable biases in AI training data. It is also essential to evaluate the model and its operational lifecycle.

Firms must document and address biases instead of embedding bias directly into the algorithms. Documenting inherent bias in data and building methods to infer results will help set the right procedures to minimize potential risks.

Another practice is to analyze the data’s subpopulations to determine if the model performs equally across different groups. Lastly, monitoring the models after deployment is essential as they drift over time.

2. How to Achieve Privacy and Security

Firms must assess, classify, and monitor data as per its sensitivity. They must develop a data access and usage policy and implement the principle of least privilege. Moreover, check for incentivized adversary attacks and the potential impacts.

Create a team that will test the system to identify and mitigate vulnerabilities. More importantly, note new developments in AI attacks and their security.

3. How to Achieve Transparency

Use small inputs needed to obtain the model’s desired performance. It helps to accurately pinpoint where the correlation or the causation between variables came from.

Prioritize explainable AI methods over difficult models, then determine the level of interpretability with experts and stakeholders.

Lastly, firms must test the AI solutions to ensure the result is true and aligns with RAI principles. This helps ensure that it is unbiased and accurate.

Also Read: Navigating the Responsible AI Landscape: What Firms Need to Know


RAI aims to harness AI’s potential while mitigating risks and ensuring the implementation of ethical considerations. It enables firms to design human-centered AI solutions. RAI also encourages firms to

  • Address limitations, flaws, and possible issues and convey them to stakeholders and users.
  • Track and monitor deployed models based on changing business needs, data, and system performance.
  • Validate data to check for wrong values, missing values, biasedness, training skew, or to detect drift.

This enables the firm to commit to ethical practices, transparency, accountability, and ongoing AI development and deployment improvement.

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Apoorva Kasam

Apoorva Kasam is a Global News Correspondent with OnDot Media. She has done her master's in Bioinformatics and has 18 months of experience in clinical and preclinical data management. She is a content-writing enthusiast, and this is her first stint writing articles on business technology. She specializes in Blockchain, data governance, and supply chain management. Her ideal and digestible writing style displays the current trends, efficiencies, challenges, and relevant mitigation strategies businesses can look forward to. She is looking forward to exploring more technology insights in-depth.

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