The 10 Steps to Educate your Company on AI Fairness

Elevate your enterprise data technology and strategy of your company with these 10 Steps to Educate your Company on AI Fairness.

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educate your company on AI fairness
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In today’s era, it is important to educate your company on AI fairness. Artificial intelligence is increasingly being used by companies, and trust is becoming an issue.

Traditionally, an algorithm is considered fair or impartial if its results are independent of various variables, including variables considered sensitive, such as different characteristics of individuals (gender, ethnicity, sexual orientation, disability, etc.).

The following are 10 practical interventions companies can use to educate your company on AI fairness. The initiatives include developing an AI fairness charter and implementing training and testing.

AI Fairness: What is it?

We live in a world powered by data and artificial intelligence (AI). COVID-19 variants can be predicted in advance, and we can opt for the quickest route. Most consumers trust the algorithms powering these experiences in many different domains.

This trust can, however, be easily broken. Consider a recruiting system that punishes applications that contain the word “woman” due to unrepresentative training data, or a credit-scoring system that ignores real-world evidence of creditworthiness, as a result, certain groups get lower credit limits or are denied loans.

Education and training about AI fairness are not keeping up with technology’s rapid advancement. As a result, it is imperative to educate your company on AI fairness. Training, developing, implementing, and marketing these data-driven experiences frequently overlook the third- and fourth-order implications of their hard work.

AI practitioners, researchers, and corporate advisers form part of the World Economic Forum’s Global Future Council.

We propose 10 practical interventions to educate your company on AI fairness.

1. Assign responsibility for AI training

The AI ethics officer (CAIO) should work with a cross-functional ethics board (representing public relations, public relations, communications, and HR) to implement AI education activities. A CAIO should also be the institution’s “ombudsman,” and personnel can approach him or her with concerns about fairness. For visibility and implementation, this role should report directly to the CEO.

2. Describe your organization’s fairness

Then, have all departments using AI-complete a fairness charter template according to their needs. Managers of business lines and providers of products and services are particularly in need of this information.

3. Maintain supply chain fairness with AI

Similarly, require suppliers who provide products and services that include AI – such as a recruiting agency that might utilize AI to screen candidates – to complete and adhere to an AI fairness charter. The procurement function and suppliers should pay particular attention to this.

4. Training staff and stakeholders by using a “learn by doing” method

All employees should receive mandatory training and certification on AI fairness principles. It is the same as how employees are required to sign codes of business conduct. Provide training to technical staff on how to build models that adhere to fairness principles. Training should directly address issues facing the organization using insights from AI fairness charters. Ensure that the ethics board reviews the course content regularly.

5. Plan for fairness and AI in HR

An HR AI fairness plan should include a yearly review by HR to assess the diversity of the team working on data-driven technologies and AI, and also an explicit review and update of the competencies and skills currently advertised for key AI-relevant development roles (product owner, data scientist, and data engineer), to ensure awareness of fairness is part of the job description.

6. Before launching any tech, ensure that it is fair

Before any AI algorithm can deploy, departments and suppliers should build fairness tests and publish the results internally. Simulate users from the group that may be unfairly treated by the data bias, and monitor their results. The product team can use this method to iterate on their product or service before it goes live. Microsoft Fairlearn, an open-source tool, can also provide the analysis for a fairness outcome test.

7. Describe your approach to AI fairness

Whenever you release a new or updated product or service, conduct fairness outcome training sessions with your customer- and public-facing staff. Customers and marketing teams will find this particularly pertinent.

8. Establish a standing item in board meetings for AI fairness

The chief AI ethics officer and ethics board should report on the progress and adherence, and address themes raised. In addition, the results of high-priority fairness outcomes tests

9. Ensure that the education sticks

Track and report on participation and completion of AI fairness activities regularly. Management of fairness has demonstrated real business value, as well. Make AI platforms and software more equitable by communicating these updates to department heads. The organization is also more efficient and productive.

10. Do not forget to document everything

Ensure employees and suppliers trained in AI fairness, as well as communicating it at high-profile events, including customer and investor events.

Also Read: Top 10 Best Business Plan Software in 2021

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