As artificial intelligence (AI) becomes more ubiquitous, it's essential to consider the biases built into the algorithms that power these systems. So the power lies in the people that create it, and there is no accountability. AI is only as objective as the people and data used to train it, built on the past of the person who created it: their traumas, their experiences, their prejudices, their biases, and their racist beliefs.
So if the people and data are biased, the AI will also be biased.
What is Artificially Intelligent?
AI employs algorithmic logic to allow a program to "think" through a task, learn from it, and self-improve in future rounds of that task. AI theory combines philosophy, mathematics, linguistics, cognitive neuroscience, psychology, computer engineering, and control theory.
Artificially intelligent (Al) systems are already present in our everyday lives, making decisions that impact everything from what ads we see online to our self-driving cars' routes to facial recognition systems. These unregulated algorithms are making decisions from who gets into college to who can own homes.
Only nine companies worldwide are developing AI: Alibaba, Amazon, Apple, Baidu, Facebook, Google, IBM, Microsoft, and Tencent.
As AI technology develops and becomes increasingly ubiquitous, it is imperative that we deeply examine the potential biases that may be built into these systems and create regulations that protect us.
These biases can have far-reaching consequences, as AI systems are designed to replicate and reinforce the values of those who create them. In other words, the biases of those who design and build AI systems can be unwittingly transferred into the systems themselves.
So, what can be done to mitigate the issue of bias in AI?
1. Diversify the people and data sets used for training AI algorithms. One of the most effective ways to mitigate bias in AI systems is to ensure that the people and data sets used to train these algorithms are diverse and represent different societal groups. This means collecting data from a range of sources, including underrepresented populations, and ensuring that the data is free from any inherent biases.
2. Encourage diverse perspectives in the development of AI systems. Another way to address bias in AI is to promote a more diverse range of perspectives in developing these systems. This means bringing in individuals from different backgrounds, experiences, and areas of expertise to help design and build these systems.
3. Implement thorough testing and monitoring of AI systems. It is essential to monitor AI systems for bias and unintended consequences continually. This can be done by setting up testing protocols and using metrics to assess the impact of AI decisions. Establishing a system for reporting and addressing any instances of bias is also essential.
4. Foster transparency and accountability in AI systems. To mitigate the risks of bias in AI systems, transparency is vital. AI developers should be transparent about how their systems are designed and how data is collected and used. Additionally, accountability mechanisms should be in place to ensure that AI systems are being used ethically and fairly.
With the increase in AI making decisions in life, who gets hired, who gets fired, or who receives a home love, we must address the issue of discrimination in these systems. We can work to mitigate the risks of bias in AI and ensure that these systems are fair and inclusive for all by evaluating the current systems, demanding transparency, diversifying people and data sets, promoting diverse perspectives, implementing testing and monitoring, and fostering transparency and accountability we can ensure that we don't let the progress we have made as humans against racism isn't a fight we have to make against machines.