AI Without Bias: Ensuring Fairness and Trust in Technology

AI Without Bias: Ensuring Fairness and Trust in Technology
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Given how at a global level, examples of facial recognition technology misidentifying people of colour or AI-based credit scoring systems discriminating against low-income communities are emerging, it is imperative for organizations to urgently prioritize identifying and mitigating bias to avoid harmful outcomes and foster trust in AI technologies

AI has become an indispensable part of our lives, powering everything from virtual assistants like Alexa and Siri to personalized recommendations on e-commerce sites and streaming platforms. As our reliance on AI grows, so does the challenge of addressing AI bias—a byproduct of evolving systems. Gartner predicted that by 2022, 85% of AI projects would deliver flawed results due to biases in algorithms, data, or management teams. Now in 2024, with increasing AI adoption, addressing and mitigating AI bias is a critical priority for organizations to ensure accurate and equitable outcomes.

Dymystying AI Bias

In simple terms, AI bias refers to the influence of human prejudices on the data or algorithms used to train AI systems, leading to skewed and potentially harmful outputs. Such biased outcomes can have serious consequences, tarnishing an organization’s reputation and eroding trust. When AI systems produce unfair or discriminatory results, it can significantly damage a brand’s image, highlighting the urgent need for companies to actively identify and mitigate biases in their AI models to ensure fairness and accuracy.

Unchecked AI bias can compromise the effectiveness of AI systems, leading to inaccurate outcomes that not only hinder business performance but also perpetuate inequality. For example, AI systems used for recruitment may unfairly favour male candidates over female ones due to biased training data that reflects historical gender imbalances in certain industries. In India, there have been cases where AI-driven recruitment tools have shown bias against candidates from certain regions or with specific educational backgrounds, perpetuating existing disparities in employment opportunities. According to The Wall Street Journal, “As use of artificial intelligence becomes more widespread, businesses are still struggling to address pervasive bias.”

While quoting the Dell Technologies Innovation Index Report, Anil Sethi who is Vice President, Infrastructure Solutions Group, Dell Technologies India, says, “95% of businesses are training or upskilling their employees to use new technology such as Generative AI. Bias in AI happens because of many things, including the data used, the way algorithms are designed, and the people and culture behind the technology.”

Mitigating AI bias in enterprise technologies is crucial to maintaining brand value, preventing financial losses, staying away from legal penalties, avoiding extensive lawsuits, and protecting brand image. “Developing AI systems that are transparent about how decisions are made and that can explain their reasoning in understandable terms can help build trust and accountability.” says Navdeep Narula, Executive Director, Mobility & DigiOps, Ingram Micro India.

The quality of data is the cornerstone in laying the foundation for an unbiased AI solution. “Implementing multiple checkpoints during the training process allows teams to regularly assess and fine-tune models, ensuring that biases are identified and mitigated early. Therefore, the emphasis on quality data input cannot be overstated, as it forms the foundation upon which these strategies are built,” stresses Sameer Bhatia, Director of Asia Pacific Consumer Business Group and Country Manager for India & SAARC, Seagate Technology.

Addressing AI bias continues to be a challenge unless organizations do not follow global standards or benchmark their solutions against industry norms. “Apart from benchmarking models for accuracy, they should also be evaluated or benchmarked against fairness metrics that can help detect bias,” interjects Sujatha Iyer, Manager-AI in Security, Zoho Corp.

Categories Of AI Bias

AI bias can be broadly categorized into societal and data biases, posing significant challenges for AI adoption. Biases can arise from the data used to train models or the way they are programmed. For example, AI recruitment tools trained on male-dominated data may favor male candidates over females, reflecting data bias. Similarly, biased Human-Machine Interfaces in performance management can perpetuate societal biases. In fields like medicine, lack of diverse data, such as varied skin tones in detecting skin cancer, can lead to biased outcomes. Addressing these biases is crucial for fair and effective AI systems.

AI bias can stem from various factors, such as training models on unsuitable datasets, designing algorithms with inherent biases, unsupervised user interactions, and lack of diversity in AI development teams. These issues can lead to models that reflect or even amplify existing societal prejudices, resulting in biased outputs and unfair outcomes. Addressing these root causes is essential for creating more equitable and accurate AI systems.

Check out these AI biases based on different causes:

●       Sample Bias arises when the dataset used to train a machine-learning model is not sufficiently representative or large to affluently train the learning model.

●       Algorithm bias springs from inherent issues within the algorithm itself that affect how it performs calculations and generates machine-learning computations.

●       Measurement bias occurs when underlying data reflects inaccuracies or inconsistencies in how it was collected or assessed.

●       Prejudice bias arises when the dataset used to teach the learning model is inherently discriminatory, prejudicial, or based on stereotypes.

●       Selection bias is akin to sample bias and happens when the data used to coach the machine learning model is insufficiently representative or large.

●       Exclusion bias occurs when important information is missed from the dataset that the machine learning model is using. This mostly happens unintentionally, or if the developers erroneously fail to consider the relevance of data as meaningful.

●       Recall bias manifests during the data labelling phase, leading to inconsistently applied labels due to subjective observations.

How can AI Bias Impact Enterprises

For businesses, addressing AI bias is both a social responsibility and a commercial necessity. A biased AI algorithm can affect a brand's reputation and business prospects. “Organizations need to invest in continuous monitoring and evaluation. They need to regularly evaluate the AI system using fairness metrics (e.g., demographic parity, equalized odds) in addition to traditional performance metrics. They also need to implement systems to detect data drift and model drift, which can indicate that the model's performance or fairness is degrading over time,” suggests Arun Chandrasekaran, Distinguished VP Analyst, Gartner, Inc.

Moreover, a partial AI system can lead to inaccurate predictions and poor decision-making that results in erroneous outcomes. For example, if a hiring algorithm is biased with candidates' age limits, then it might overlook qualified candidates, leading to a less competent workforce. Also, if a firm has more male employees than female, while following the pattern a biased AI can find female candidates unsuitable, causing an imbalance in the workforce. “In a country like India where we have diverse regional, linguistic, and socio-economic environments, having developers from different backgrounds ensures that the AI system reflects the needs and realities,” recommends Sameer Bhatia of Seagate Technology.

Operational Risks - AI bias can lead to unfair treatment of consumers, resulting in dissatisfaction and loss of trust, which is difficult to rebuild. This can negatively impact brand value, cause financial losses, and expose organizations to legal penalties and lawsuits, creating long-lasting complications.

Reputational Damage - The consequences of reputational damage due to AI bias can severely impact organizations. In worst cases, it might cause customers’ and partners’ loss of trust in the brand, resulting in getting hands-off business and market share. Consequently, maintaining an unbiased and fair AI is crucial to sustaining the reputation and business of a brand.

From Bias to Balance

Experts from the Trustworthy and Responsible AI Council advocate for operationalizing values like fairness and transparency, and incorporating revised standards during AI development and deployment to effectively mitigate the risks of bias.

“Identifying and mitigating bias during the AI model training process is crucial to ensure fairness, accuracy, and generalizability. The effective strategies to mitigating boas includes ensuring diversity in data collection, data auditing, usage of bias detection tools in data inputs to AI models and by having human-in-the-loop for data vetting,” says Arun Chandrasekaran, Gartner, Inc.

It is important to constantly monitor AI systems’ outcomes and correcting biases in all forms to ensure systems are bias free over a longer period of time. “Bias in AI can only be introduced through data, since there is already so much bias already in the data around us, it is extremely crucial to closely monitor data and outcomes during the AI model training process,” opines Swapnil Shende, Associate Research Manager, AI Research, IDC India.

●       Authentic Data Source - Researchers at NIST highlight that machine learning processes require large datasets, leading developers to use readily available data, which may not reflect real-world scenarios. For instance, data from surveys or social media often represent specific user groups rather than the general population. This mismatch can result in sampling bias, as the AI models built on such data may not accurately represent the broader population or phenomena.

●       Data Relevance - In machine learning, models reuse existing datasets, which can lose relevance over time and become disconnected from their social context. It’s essential to assess the background of data sources to ensure accurate data collection, maintenance, and demographic representation. Another critical step in mitigating AI bias is balancing statistical representation within datasets. Documenting and communicating how AI outputs will be applied—whether for prediction, benchmarking, or classification—is crucial to maintaining transparency and relevance in AI model deployment.

  1. Decisions Made By Design Team And Developers - In addition, NIST reveals through its publication that systemic institutional biases are compounded by the assumptions and decisions made by the AI design, and development teams in choosing the datasets to train the model. Their prejudices affect the decisions made by them leading to include or exclude what and who gets counted or is being left.

●       System Left Unsupervised - Validation is also important to ensure the system doesn’t get used in unintended ways. Also, during the deployment process, if the system is being used in a way that developers are not considering, then deployment bias arises.

●       Data Ingestion - The development team should also check for data ingestion, which includes the vigilance of data truncation or data leakage. After deploying the data to the model, constant monitoring is also required to gather the outlook and fine-tune the model. So, data and analytics professionals must cultivate self-awareness and integrate mindful practices into AI governance to prevent the common biases present in human-created AI algorithms.

●       Diverse Data Collection - To better represent the diverse communities it serves; generative AI models need to integrate a wider spectrum of human data. AI works more accurately when they are combined with diverse human experience and intelligence. “Diversity within AI development teams is foundational to building inclusive AI systems. A team composed of individuals from varied backgrounds, disciplines, and perspectives is more likely to recognize and question underlying assumptions that could lead to biased outcomes,” opines Anil Sethi, Vice President, Infrastructure Solutions Group at Dell Technologies India

●       Algorithmic Transparency - Transparent AI doesn’t have to do anything with publishing AI algorithms, rather it enables humans to articulate what is going around in the AI model. This is because, when the AI makes mistakes, human interruption is required.

●       Bias Detection Tools - To maintain fairness as new data is introduced, it's important to implement tools designed to detect and measure biases in AI models. “Regular testing against diverse datasets helps identify biases that may emerge over time. Additionally, continuous monitoring of algorithms and data streams is crucial for maintaining unbiased outcomes. This ensures that AI systems remain fair and ethical, while addressing concerns related to data privacy and potential misuse,” emphasizes Anil Pawar, Chief AI Officer, Yotta Data Services.

●       Inclusive Development Teams - Diverse teams can offer a broader range of viewpoints, leading to more inclusive and ethically sound AI solutions. “Diverse AI development teams play a crucial role in reducing bias by bringing varied perspectives that help identify and mitigate biased assumptions and design flaws in AI systems. This diversity helps ensure AI systems are more inclusive and fairer across different demographics,” adds Anil Pawar of Yotta Data Services.

●       Regular Audits And Assessments - Another fundamental aspect of mitigation of AI bias from models is regular audits and assessments of AI systems to ensure they operate fairly and ethically. “Continuous monitoring and auditing processes are key to ensuring AI systems remain fair and unbiased over time. This includes regularly testing model performance with diverse real-time data to detect emerging biases, conducting algorithmic audits at various stages, and establishing feedback loops to promptly identify and mitigate issues,” adds Swapnil Shende of IDC India.

Conducting regular reviews allows organizations to promptly identify and correct biases that could influence decision-making. These audits assess AI algorithm performance across various demographic groups and contexts, pinpointing areas where biases may exist. This proactive approach helps ensure fairness and accuracy in AI-driven processes.

The Evolving Landscape for Policy and Regulation around AI

Policy and regulation plays an important role in the future of AI-related bias. According to Navdeep Narula, Executive Director, Mobility & DigiOps, Ingram Micro India, “A good example can be of an Indian actor, Anil Kapoor, who won a landmark case on the subject of Artificial Intelligence in India. The Delhi High Court restrained the misuse of the actor's name, image, voice, and other attributes of his persona, including his “jhakaas” catchphrase.” Navdeep underlines that the order was passed against several websites and platforms in a lawsuit by the actor, alleging unauthorized exploitation of his personality and celebrity rights for commercial use using Artificial Intelligence.

As AI technology advances, social, legal, moral, and regulatory challenges have emerged. Solving the problems that occurred due to AI bias requires a collective effort. So the most difficult part of AI bias is to eliminate the bias from AI algorithms. “Policy and regulations impact AI bias by establishing standards for fairness, transparency, and accountability, and by enforcing data quality and ethical guidelines. Future regulations are likely to include mandatory bias audits, explainability requirements, and stricter data protection laws to ensure AI systems are fair and non-discriminatory,” emphasizes Arun Chandrasekaran, Gartner, Inc.

Anil of Yotta Data Services also emphasizes the need of effective governance. According to the IBM study, 7 out of 10 Indian CEOs surveyed say trusted AI is impossible without effective AI governance in organizations. In contrast, only 4 in 10 Indian CEO respondents say they have good generative AI governance in place today.

From a legal perspective, the European Union's General Data Protection Regulation (GDPR) includes provisions addressing automated decision-making and profiling, protecting individuals from significant legal or personal impacts caused by computer-based decisions. Countries like Canada and the United States are exploring regulatory frameworks to tackle concerns around AI bias. Additionally, the United Nations has launched the AI & Global Governance Platform to assess global policy challenges. However, India currently lacks a unified approach to specifically regulate AI bias and its consequences on a national level.

Global regulatory bodies and cross country collaboration to promote transparency and fairness are critical to mitigating AI bias in systems, solutions and applications. “Regulations encouraging collaboration between industry and other stakeholders promote a more balanced approach to AI development. By involving diverse perspectives and expertise, these collaborations can help identify and mitigate biases that may arise from a single viewpoint,” expresses Anil Sethi of Dell Technologies India.

In nutshell

AI bias affects individuals, society, and organizations, but can be mitigated by using diverse datasets and advanced tools. All companies, regardless of size, should adopt best practices from leaders in ethical AI development. Last year, Elon Musk announced his plan to counter perceived bias in ChatGPT with his own AI, "TruthGPT," aiming to create a truth-seeking AI that understands and respects humanity. Addressing AI bias, promoting transparency, and integrating ethics are crucial to harness AI’s transformative potential while ensuring a fair and inclusive future.

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