Transforming Data/Model Governance using AI and Machine Learning

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Across AI and machine learning applications, the effectiveness of their algorithms is limited to the quality of the data fed into these models. Flawed, inaccurate, or biased data can lead to incorrect outcomes and results.

65% of business executives are concerned about data bias in their organizations, while only 13% are proactively addressing this issue. With the increased adoption of AI technologies, 78% believe that data bias will become a bigger concern.

In 2024, data security and governance are emerging as the priorities for business enterprises – even more than AI adoption. This is because companies are realizing the value of data governance and proper data access to unlock business value from data. According to the 2024 State of Data Security report, 80% of respondents are prioritizing the development of governance structures for data, while only 20% are prioritizing AI integration into their business processes.

Data Bias in AI: Real-world examples

Here are some real-world examples of data bias in AI and machine learning models:

  1. Healthcare
    The healthcare industry has reported an inaccurate representation of data related to women and minority groups. For instance, AI-powered diagnosis systems have recorded lower accuracy for black patients as compared to white patients.
  2. Applicant tracking and hiring
    AI models with natural language processing (NLP) capabilities have delivered inaccurate results in the domain of applicant tracking and hiring. In a recent case, Amazon discontinued its AI-based hiring model after it was found to favor applicants who commonly used words like “executed” and “captured” in their resumes.
  3. Image generation
    Recent academic research found bias in the Generative AI image generation application, Midjourney. For instance, when generating images of people in specialized professions, the tool displayed both younger and older professionals. However, the older professionals were all men, thus reinforcing gender bias against working women.
  4. Search engine advertisements
    Search engine job advertisements were found to have gender bias for various job roles. For instance, Carnegie Mellon University recently found an online advertising system displaying high-income positions more often to male candidates than female candidates.

How AI is transforming data governance

Data governance is emerging as the framework for ensuring the ethical and responsible way to manage AI-powered data models. Responsible AI is now essential for organizations looking to develop and deploy AI-powered models.

As a core concept, a data governance framework is all about the set of processes, policies, and industry standards used to govern organizational data. Effective data governance is closely linked to the quality of AI models. While AI models can self-learn from data, biased data can lead to error-prone models and decision-making.

Here are some of the benefits of AI-enabled data governance:

  1. Improved data quality
    The availability of high-quality data is critical for any business strategy. AI-powered algorithms can easily detect and rectify any data anomalies or errors. With machine learning, data models can spot hidden data patterns or biases.
  2. Data compliance
    Among the key tenets of data governance, data compliance in any organization requires constant monitoring and vigilance. AI-enabled models can monitor any violation of data security or privacy regulations, thus ensuring data compliance.
  3. Data democratization
    To promote effective governance, organizations need to encourage more employees to leverage data-driven insights in their daily work. This democratization process helps enterprises develop a “data-centric” culture.

At Onix, we can help you develop a robust data/ model governance framework to tackle issues like data bias and to incentivize accountability and responsibility.

Do you want to learn how to implement this data governance framework? Download our latest eBook on “Data/Model Governance in the Age of AI and Machine Learning” by filling out the online form.

Reference links:

Data Bias: The Hidden Risk of AI (progress.com)





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