In the workplace, artificial intelligence is proving to be a transformative force. Its ability to automate tedious activities and transform massive volumes of data is unparalleled (as of yet). Other superpowers of it? AI can personalize marketing campaigns, expedite the hiring process, offer data-driven insights into employee performance, respond quickly to consumer inquiries, evaluate and track regulatory compliance, and more.
Even though AI has many advantages in the workplace, AI bias is a developing issue that needs to be addressed.
Why are individuals so concerned about bias in AI?
AI has enormous potential in the workplace. It may impact choices that affect our careers, from the interviews, employment, and performance reviews we obtain to the possibilities for training and professional growth that are provided. However, those decisions may result in discrimination against specific groups of individuals if biased AI systems make them.
How can prejudice in AI arise?
AI programs do not create themselves. AI development involves a significant human component. Unconscious bias in the workplace is frequent. Additionally, this may result in the introduction of unconscious bias into AI systems’ algorithms and data. As a result, AI may inadvertently prejudice against particular groups, maintaining social injustices.
Is this concern justified?
Sure, in a nutshell. The fear surrounding artificial intelligence bias stems from actual instances of prejudice in AI that result in unfair treatment and discrimination. Consider the recent legal cases that raise ethical concerns regarding using AI in online marketing, chatbots for customer support, hiring tools, and financial services compliance and risk assessment. Bias in AI systems can potentially have severe and far-reaching effects on human lives, jobs, and well-being if allowed to continue unchecked.
What, then, is doable? Read on for three practical tactics your company can implement to minimize bias in an AI-driven workplace and manage AI ethics.
Test and audit AI systems frequently.
An important lesson recently acquired by a worldwide HR team at a big retailer is the necessity of regularly testing artificial intelligence screening systems for bias, especially if a variety of regional markets embrace them. Following an internal hiring process for UX designers throughout Australasia, hiring managers in the region observed an unsettling trend. Even though the number of female candidates was fairly balanced, 80% of the “top” candidates were men.
As a result, the local HR staff decided to stop using AI screening technologies for the entire organization. Bias in the data used to evaluate candidates was discovered during a thorough assessment, primarily because the input of mostly male resumes from North America made up the data utilized to train the application screening system. The audit was illuminating, so AI recruitment tools are now regularly tested in all markets.
How can organizations reduce bias in AI systems that are comparable to this?
Take these valuable actions:
- Verify that AI providers share their findings regularly and incorporate Key Performance Indicators (KPIs) centered on equity, accuracy, and transparency.
- Establish regular reporting showcasing advancements and any attempts to reduce bias in artificial intelligence.
- Provide precise protocols for resolving detected problems or prejudices and a defined mechanism for adjustments and ongoing development.
- When testing AI systems, ensure accountability and openness and make the results available to all pertinent parties.
When acquiring artificial intelligence solutions:
- Incorporate provisions within contracts that grant testing and audit rights, enabling your organization to carry out impartial evaluations or engage outside auditors.
- Provide rewards for reaching or surpassing testing and evaluation standards and sanctions for noncompliance.
- Promote cooperation with the vendor to create a mutually agreed-upon regular audit schedule and testing and assessment process.
AI systems must adapt to changing user behavior, data, and ethical norms. You’ll need to test AI systems frequently to prevent prejudice and discrimination in delicate areas like recruiting, financial services, and compliance.