AI Transparency is the principle mandating that artificial intelligence (AI) operations can be easily understood by humans. As generative AI, the subset of AI technology that leverages machine learning techniques to generate new output from training data, becomes more integrated into our daily lives, transparency and trustworthiness have taken center stage. IBM has made significant strides in championing this cause, focusing on research that increases accountability within AI technology.
Generative AI, while revolutionary, is not without its challenges. One of the main hurdles includes the detection and tracing of content back to its source. In recent years, advancements in AI models have led to the creation of eerily realistic content, stretching from text to audio and even video. Identifying whether content was human or AI-generated and tracing it back to its origin has become an arduous task.
In response to this, IBM collaborated with Harvard University to develop an early AI-text detector called GLTR. The tool analyzes the statistical relationships among words to distinguish whether text has been automatically generated by an AI. It gave researchers a fighting chance in the battle against AI-impersonating humans.
Not stopping there, IBM unveiled RADAR, an advanced detection tool designed to identify AI-generated text that’s paraphrased to deceive detectors. The creation of such a tool signifies a major step forward in curbing the malicious use of AI technologies.
IBM further ensures the safe application of generative AI by establishing rigorous controls to prevent a data leakage. The company understands that data is a powerful tool that can be weaponized if it falls into the wrong hands, and as such, they take strong measures to secure this information.
Attribution in AI, which refers to identifying the original models that produce a given text, is paramount in fostering accountability. The infiltration of Deepfakes and misinformation campaigns has highlighted the urgent need for credible AI attribution. IBM’s Matching Pairs Classifier comes in as a solution to this. This innovative tool utilizes machine learning to recognize the model origin associated with a text piece, bolstering the trustworthiness of AI-generated content.
IBM’s commitment to fostering a trustworthy AI environment is starkly evident. One of their flagship offerings is the AI Fairness 360 toolkit, which provides an open-source library to assist in mitigating bias within AI models. Furthermore, IBM is planning to enhance AI transparency via WatsonX governance – an initiative aimed at streamlining AI workflows.
In conclusion, transparency and attribution are vital in the expanding field of AI. IBM’s relentless pursuit of creating more accountable and transparent AI technology, coupled with the intent to make their transparency tools accessible to all, establishes IBM as a torchbearer in this important agenda.
Would you like to delve deeper into the world of AI technology? Feel free to share your thoughts in the comments section below or subscribe to our newsletter for more updates on AI advancements. Your feedback is integral in our continuous exploration of this revolutionary technology landscape.
Up until working with Casey, we had only had poor to mediocre experiences outsourcing work to agencies. Casey & the team at CJ&CO are the exception to the rule.
Communication was beyond great, his understanding of our vision was phenomenal, and instead of needing babysitting like the other agencies we worked with, he was not only completely dependable but also gave us sound suggestions on how to get better results, at the risk of us not needing him for the initial job we requested (absolute gem).
This has truly been the first time we worked with someone outside of our business that quickly grasped our vision, and that I could completely forget about and would still deliver above expectations.
I honestly can't wait to work in many more projects together!
![The ‘Giveaway Piggy Back Scam’ In Full Swing [2022]](https://www.cjco.com.au/wp-content/uploads/pexels-nataliya-vaitkevich-7172791-1-scaled-2-683x1024.jpg)
Disclaimer
*The information this blog provides is for general informational purposes only and is not intended as financial or professional advice. The information may not reflect current developments and may be changed or updated without notice. Any opinions expressed on this blog are the author’s own and do not necessarily reflect the views of the author’s employer or any other organization. You should not act or rely on any information contained in this blog without first seeking the advice of a professional. No representation or warranty, express or implied, is made as to the accuracy or completeness of the information contained in this blog. The author and affiliated parties assume no liability for any errors or omissions.
