In 2019 Facebook was sued by the US Department of Housing and Urban Development because its ad-serving algorithms enabled advertisers to discriminate based on characteristics like gender and race. In 2018 Google decided not to continue its AI contract with the Department of Defense after employees raised ethical concerns. The same year Amazon had to cease its artificial intelligence–powered recruiting tool after it discovered the system was biased against female applicants. Two years earlier, biases against black defendants were revealed by ProPublica investigation in a recidivism assessment tool that used machine learning.
AI is widely used nowadays and drives positive changes in many industries, such as recruitment, healthcare, retail, education, finance etc., bringing huge benefits to humanity. Still, the usage of artificial intelligence raises a number of ethical concerns.
While one believes that the main advantage of AI tools is that they might be impartial and objective, helping us to make data-based decisions, others are concerned about their proper training. The key portion is that discrimination comes from training upon data that has biases present in it, so the network learns those biases.
This is how AI bias happens
The definition of bias is – a disproportionate weight in favor of or against an idea or thing, usually in a way that is closed-minded, prejudicial, or unfair.
Biases can creep into AI in several different ways. For example, the training data artificial system uses can reflect biased human decisions. Also, the training data can reflect historical, social, racial, gender inequities.
Let’s have a look at why your AI is biased.
The vast majority of AI applications developed nowadays are based on the deep learning algorithms. Depending on what data is used to train these algorithms and how they find patterns in data influences the purity of results.
- One of the major reasons for bias in AI is that not enough data was collected. Basically, it means that the collected data is limited to a representation of a certain demographic group and is not diverse enough to make comprehensive predictions. Another way to think about this is that the network is unable to generalize because it’s data was limited to that specific demographic, it’ll only really be able to make predictions on the same type of data.
- Under-representation of some groups may also occur when AI systems learn patterns from the data that is generated by humans with their in-built biases. In this case the AI system will also reflect the biases. So basically, the AI is as good as the data used to train it. The AI learns the relations between features you are using while creating algorithms. For instance, if you are using features as race, ethnicity or gender, the AI algorithms may become biased and this will perpetuate injustice.
- Another possible reason for biased AI tools is a lack of diversity. If companies that develop such products are not diverse enough it may bring more bias into the process. A company could implicitly increase it’s data diversity by having a diverse team of representatives of different racial, ethnical, cultural and gender groups.
What is Ethical AI and how to create it?
Ethics in AI is the principle of designing AI systems using algorithms that ensure the system is able to respond to situations in an ethical way. Ethical AI tools are impartial and do not possess any sort of biases.
Use representative data
The AI is as good as the data it uses. If the data you are using reflects the history of our own unequal society, we are in effect asking the program to learn our own biases. Basically, the quality of data you are using to train and test AI algorithms influences the outcomes. To ensure that you are building an ethical and bias-free artificial intelligence, train it using a full-spectrum of data.
Data vetting is another fundamental component. Vetting is the process of checking to assure the quality of the data we import. Even when data is imported through an automated application there could be errors or missing data in the imported series. Data vetting is also an important step that is teasing out bias that could be in your data. That’s why it’s essential to build systems to detect these types of errors and prevent them from getting into the dataset.
As the system is exposed to more and more data, it gets better at learning and is able to optimize the algorithm to achieve better performance. As a result, new insights are gained and better decision rules are learnt.
Test and validate
Test and validate your tool regularly if you want to make sure that the AI is ethical and doesn’t possess any sort of biases. You can also assess your data using third-party tools. Such tools will help you to assess bias at various stages. For an effective verification and validation, the third-party needs to understand the entire lifecycle of the AI-enabled system: from evaluating the relevance of the training datasets, to analyzing the model’s goals and how it measures success.
One more step to validate your data is model interpretability, which is understanding why things went wrong when they went wrong, and having tools in place to analyze and understand your model. Explainable AI leads to ethical AI!
Hire diverse teams
Another step to take in order to prevent bias against certain groups is to create a diverse, inclusive and curious environment in the workplace. When building your team, hire people from different backgrounds, races, ethnicities, cultures. They are likely to ask tough questions about the organization’s ethics, diversity, and inclusion and take into account their background while creating the tool. Keep in mind that it’s important to foster an environment where people can be open and communicate with each other, so that people can ask those tough questions without worrying.
Make sure that there is an alignment on ethics in your company and disparate groups of people share value and understanding about ethics while creating AI systems. As a result, this is going to help you to ensure that your AI data sets are inclusive and avoid bias in your AI and later on, discrimination.
Artificial Intelligence is a powerful technology that has seen phenomenal growth latey. It’s driving innovations and boosts performance in a wide variety of industries. It helps to make data-driven decisions, automate business processes, and deliver results more quickly and efficiently. Because AI is a real game-changer, the question about its ethics arises nowadays even more often than before.
Talking about the HR industry, AI is able to bring impressive improvements to the recruitment process. The major thing that companies developing AI tools need to be aware of is that Ethical AI must avoid biases. AI recruitment tools are created to help improve diversity and promote equality in the workplace. If built in the right way using validated data created by a diverse team that has tested and validated it regularly, it can excel your business processes far beyond your expectations.
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