#16: Neutralizing Gender Bias, Amplifying Voices, and Tracking Cotton Diseases from Space
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Jump right into Issue #16 of Good AI Vibes, your treasure trove of the most exciting AI adventures for businesses. Stay with us and explore innovative ways to jazz up your work with AI.
And now, the latest scoop: 👇
🧑💼 Fair Play in Hiring: AI's Role in Neutralizing Gender Bias
📞 Amplifying Voices: How AI Turned Noisy Calls into Clear Insights
🌿 Beyond Human Eye: Satellite-Powered AI for Cotton Health
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Fair Play in Hiring: AI's Role in Neutralizing Gender Bias
What's the critical hurdle?
Meet Xerxes, the enthusiastic Head of People and Culture at a burgeoning multinational. Every hiring season, Xerxes faces a dilemma that's as old as the industry itself: ensuring that the company's job listings attract a diverse pool of talent. It’s crucial for the company's reputation, employee satisfaction, and bottom-line performance.
However, there's a hitch. Despite efforts to be inclusive, the applicant pool remains conspicuously imbalanced. Xerxes notices that certain job posts inexplicably attract more males, while others draw in a majority of female applicants. This inconsistency isn’t just troubling—it's a roadblock to the company’s commitment to diversity, potentially sidelining excellent candidates and narrowing the talent pool. For example, an ad for a ‘dynamic, competitive sales lead’ saw a skewed ratio of male applicants, leaving potential female talent untapped.
How was this initially tackled?
Before the world began talking about AI solutions, Xerxes and his team were trying to handle this manually. They meticulously crafted job descriptions, cautiously selecting every term and diligently analyzing job roles to avoid gender-coded language. For instance, they replaced words like ‘ninja’ with neutral terms like ‘professional.’ They believed clear, neutral language would be the beacon guiding diverse talents their way.
Why did the initial approach fall short?
The manual approach, as sincere as it was, had its pitfalls. Human oversight meant some biased language slipped through the cracks, and the problem was larger than just one or two terms—it was the general tone and context that often carried unintended signals. Despite the team's efforts, certain job posts still echoed societal stereotypes about “masculine” or “feminine” roles, inadvertently deterring a segment of qualified professionals. The solution was there, but it wasn’t bulletproof. It became clear: they needed a finer, more sensitive sieve to catch these subtleties.
How did AI revolutionize the solution?
Enter LLMs (Large Language Models), the game-changer in this scenario. This solution dives deep into the job listing’s language, structure, and phrasing, scanning for biases that the human eye might miss. It's like having a consultant with an incredibly keen sense, flagging words or sentences that could be off-putting to some applicants based on the gender coding of language.
For Xerxes, using LLMs starts with inputting the text of a job listing. The AI then scrutinizes the information, highlighting terms and phrases that carry gender bias, and suggests more neutral alternatives. Imagine the AI spotlighting the word "aggressive" and offering substitutes like "assertive" or "confident," maintaining the job post's essence without deterring half the population.
In a remarkable instance, one of the company's job listings for a top-tier position was adjusted based on LLM’s recommendations. The result? A significant uptick in applications from a diverse range of candidates, debunking myths about who aspires to what kind of role.
Main reference: Tang, S., Zhang, X., Cryan, J., Metzger, M. J., Zheng, H., Zhao, B. Y. (2017). Gender Bias in the Job Market: A Longitudinal Analysis. Proceedings of the ACM on Human-Computer Interaction.
See other references at the end.
Amplifying Voices: How AI Turned Noisy Calls into Clear Insights
What's the critical hurdle?
Introducing Rafael, the Head of Customer Service at a thriving multinational corporation. His team manages thousands of calls daily, each one a goldmine of information about customer preferences, issues, and feedback. To harness this data, these voice calls are transcribed into text using speech-to-text technologies and analyzed for insights. But here's the hiccup: many of these calls come with background noises—chattering at a cafe, traffic sounds, or even a TV playing in the background. This noise makes it a challenge to accurately convert voice to text. For instance, instead of reading "I love the new feature," the text might show "I love the mew feature" due to some background cat noise! 😸
How was this initially tackled?
Rafael's initial strategy was simple yet costly: discard the noisy calls. Yes, any call with discernible background noise was left out of the transcribing process to ensure the accuracy of the data being analyzed. It's akin to skipping the pages of a book that have smudges, even if they contain critical plot points.
Why did the initial approach fall short?
By discarding noisy calls, the company was essentially throwing away valuable data. Imagine missing out on feedback from a significant customer simply because they made the call from a bustling marketplace! Not only did this method reduce the volume of data available for analysis, but it also introduced a potential bias—only including feedback from customers in quiet environments. The richness and diversity of customer experiences were not being fully captured, leading to gaps in understanding their needs and concerns.
How did AI revolutionize the solution?
In the face of this challenge, Rafael turned to a cutting-edge AI solution. At its core, this AI system was designed to recognize and separate human speech from any accompanying background noise. It's akin to having an intelligent filter that can meticulously differentiate between the essential vocal tones of a conversation and the intrusive sounds of the environment.
With this AI tool in action, every single call was meticulously processed. The system would scan the audio, identify portions where the customer's voice was overshadowed by noise, and then work to enhance the voice while suppressing the noise. It's a bit like having an expert audio technician refining each call, but done automatically and at lightning speed.
The results were nothing short of remarkable. Calls that were once discarded due to their noisy content were now being transformed into clear, coherent text. Feedback that might have been lost, like "Your website is a bit tricky to load," was now captured with precision. Rafael witnessed a dramatic increase in usable call data, ensuring a richer and more comprehensive understanding of customer feedback. The company was now confident that they were leaving no stone unturned and no valuable insight unattended.
Main reference: Yadava G. T., B.G. Nagaraja, H.S. Jayanna, J.D. Gibson (2023). Enhancements in encoded noisy speech data by background noise reduction. Intelligent Systems with Applications.
See other references at the end.
Beyond Human Eye: Satellite-Powered AI for Cotton Health
What's the critical hurdle?
Meet Aanya, a production manager at a major cotton production company. Aanya's company owns vast tracts of fertile land, where they cultivate cotton, often referred to as 'white gold' in the agricultural world. The revenue from this cotton production plays a significant role in the company's bottom line. But there's a pressing issue: diseases, especially those affecting the cotton leaves, lead to substantial crop losses every year. To put it into perspective, 70-80% of the diseases affecting their crops are related to the leaves, and the rest are due to pests. For Aanya, this means reduced yields, wasted resources, and a direct hit to the company's profitability. Imagine an entire field being affected by a disease that went unnoticed, leading to diminished returns on investment for that entire season.
How was this initially tackled?
Before the world of AI stepped in, the primary method to tackle this issue was manual inspection. Experts in plant pathology would walk through these vast fields, inspecting cotton leaves. They would rely on their years of experience and knowledge to identify diseases and suggest remedies. For instance, upon noticing a particular pattern or discoloration on a leaf, an expert might deduce it as a specific disease and recommend a particular treatment. This hands-on approach, while valuable, was incredibly time-consuming and labor-intensive.
Why did the initial approach fall short?
Manual inspection had its limitations. First and foremost, the sheer size of the cotton fields made comprehensive inspection almost impossible. Even the most diligent experts could miss certain areas or misidentify diseases, given the subtle differences between some of them. Moreover, human fatigue and the influence of external conditions like lighting could further reduce the accuracy of disease identification. The repercussions were severe: untreated or wrongly treated diseases could spread, leading to extensive crop loss. This traditional approach, while rooted in expertise, couldn't guarantee consistency or accuracy, leaving a significant portion of the crops at risk.
How did AI revolutionize the solution?
Enter the realm of AI. Aanya’s company devised a state-of-the-art solution using deep learning to accurately identify diseases on cotton leaves. Picture this: high-resolution satellite images capturing vast stretches of cotton fields. These images are then fed into the AI system, which has been trained on numerous images of cotton leaves, both healthy and diseased. When the system processes these satellite images, it can pinpoint with precision which parts of the field show signs of disease.
For Aanya, this is transformative. Instead of solely relying on manual inspections, her company can now utilize this AI-powered solution to scan vast areas of their fields through satellite imagery. Once these images are captured, the AI swiftly scans and identifies potential outbreaks or disease clusters. With an impressive accuracy rate of 98.70%, this system ensures that even the slightest hint of a disease doesn't go unnoticed.
The implications? Rapid response to treat affected areas, leading to healthier crops and significantly improved yields. The added advantage is the vast coverage satellites provide, ensuring no leaf is left unexamined. For Aanya and her company, this AI solution is a monumental leap, safeguarding their cotton production and reinforcing its title as the 'white gold' of the industry.
Main reference: Islam, M. M., Talukder, M. A., Sarker, M. R. A., Uddin, M. A., Akhter, A., Sharmin, S., et al. (2023). A deep learning model for cotton disease prediction using fine-tuning with smart web application in agriculture. Intelligent Systems with Applications.
See other references at the end.
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