AI is quickly becoming a part of our day-to-day lives, whether at work or on a personal level; AI’s seed has been sown, and its apparent implementation is visible even to the naked eye. In the tech-driven world that we live in, AI’s contribution is mostly met with contentment and gratitude, but just like everything else, it doesn’t come without setbacks and flaws of its own. The question is, can AI data be biased? The answer is simply yes, so how do we mitigate it? This blog is going to look at the impact bias AI data has on today’s society and organizations and the steps we need to take to mitigate this through data quality and transparency.
What is AI Bias?
AI systems are fed information and trained by humans therefore if the human influence comes from a place of judgment or personal preference this will affect the overall decision-making, outcomes and reporting from the AI system, resulting in bias data.
1. Human Influence
AI systems are fed information and trained by humans therefore if the human influence comes from a place of judgment or personal preference this will affect the overall decision-making, outcomes and reporting from the AI system, resulting in bias data.
2. Biased Data
AI systems use and learn from historical data, therefore if previous data input has been tainted with social bias such as racial or gender preference then the AI data will more than likely maintain those bias tendencies in its future decision making.
3. Algorithmic Data
The design of an AI model can influence bias and can prioritize certain features or outcomes over others, resulting in a biased outcome.
The Ramifications of AI Bais
The aftermath of AI bias decision-making can be extremely harmful and have a profound, long-lasting effect on people’s lives. Here are some examples of such exploits;
1. Law Enforcement
If trained on biased criminal data, predictive or historical police algorithms may detect racial profiling or other unfair practices.
2. Hiring Algorithms
Bias-trained data can result in disadvantages when selecting applicants based on gender, race or sociogenic.
3. Healthcare
Across the healthcare sector AI systems can be used to help diagnose illness or recommend treatments but it may underrepresent various groups leading to discrepancies in the health care’s overall outcomes.
The examples shared above show the magnitude and severity of AI bias not just on a technical level but for the ethical rights and equality of mankind.
Mitigating AI Bais with Data Quality
To mitigate AI bias, we need to look at the data quality that the AI models are being taught. Improvements in quality data naturally will help build robust, accurate AI models, leading to unbiased outcomes. Here, we illustrate methods to help mitigate AI bias using quality data;
1. A Diverse Representation of Data
Quality data comes from diversity and should be practised when training an AI system to deliver an accurate representation. For example, when using facial recognition systems, images of people should be taken from several different ethnic groups to ensure a non-biased outcome.
2. Data Cleansing
Cleansing data before training an AI system plays an important role in utilizing quality data. By cleansing data mistakes can be delated, errors can be corrected, and bias can be removed.
3. Audit Continuity
Continuity and consistency in Auditing must become the norm. By auditing data on a regular basis, the detection of any unfair patterns or representatives will become clear. Allowing improvements to be made for future growth in quality data.
Enhancing Transparency to Tackle AI Bias
Transparency is a key player in tackling the profound problem of AI bias. Ensuring that AI systems are transparent will leverage organizations to help detect any potential sources of bias. Here outlines how and why transparency really can help;
1. Data and Algorithm Documentation
Documenting and storing historical datasets and algorithms that are used in AI systems is a great source of reference when looking out for bias. Records such as data collection processes and the decision-making process in algorithmic patterns. Small steps of transparency like this will help detect bias early on and can prevent it from happening in the future.
2. Explainability
It is important to be transparent from the outset with regard to the design and decision-making of the AI systems programming and development. This is particularly important when AI systems are used in sectors such as law enforcement, healthcare, and finance, as the risk implications can be much higher due to the nature of this work and the members of the public who may be involved. Using explainable (XAI) techniques can help developers and users understand how the AI system found its conclusion, making it easier to detect bias from the get-go.
3. Collaboration in Open Development
It is important to be transparent from the outset with regard to the design and decision-making of the AI systems programming and development. This is particularly important when AI systems are used in sectors such as law enforcement, healthcare, and finance, as the risk implications can be much higher due to the nature of this work and the members of the public who may be involved. Using explainable (XAI) techniques can help developers and users understand how the AI system found its conclusion, making it easier to detect bias from the get-go.
4. A Concise Ethical Framework
Using ethical frameworks within organizations should be standard practice but, at times, can be overlooked, and this is an example of how AI data can become biased. Using concise ethical frameworks in developing and deploying an AI system ensures fairness, equality and transparency, monumentally helping the depletion of biased data outcomes.
Conclusion
The implications and uncertainties involved when using biased data can be extremely damaging not only to an organization but, more importantly, to the groups of people or individuals involved. But by Understanding AI Bias and Mitigating it with Data Quality and Transparency, we are one step closer to becoming ethically sound in today’s tech-driven world. It is fair to say AI bias can be challenging and complex in certain areas, but if developed and managed in the correct way, biased data can become a thing of the past. Like anything in life, if treated fairly and with care, it will flourish. Taking a diverse, thoughtful approach in the development stage and collaborating through ethical frameworks will enhance the chances of a fairer future in the world of AI, allowing everyone to benefit from this technology and not just a select few.
Here at VE3 we have expertise in Responsible AI Development, would be more than happy to help you. We are committed to helping businesses harness the power of AI. For more information visit us or contact us directly.
