Startup ecosystem in RAI
In this section, we will discuss a few notable startups emerging in the responsible AI space and building products that keep RAI at their core.
- Parity AI: Founded by Rumman Chowdhury, Parity AI focuses on AI risk management and offers tools for auditing AI models for bias or legal compliance and provides recommendations for addressing these issues (https://www.get-parity.com/).
- Fiddler: Founded by Krishna Gade, Fiddler focuses on explainability in AI, helping to make AI model decisions more transparent. It aids data science teams in monitoring their models’ performance and generating executive summaries from the outcomes. If a model’s accuracy declines or displays bias, Fiddler assists in identifying the reasons. Gade views model monitoring and enhancing clarity as key initial steps for more deliberate AI development and deployment (https://www.fiddler.ai/ai-observability).
- Arthur: Founded in 2019, Arthur is a company specializing in AI performance, assisting enterprise clients in maximizing their AI’s potential through performance monitoring and optimization, providing explainability, and mitigating bias.
- Weights and Biases: Founded in 2017, Weights and Biases focuses on the reproducibility aspect of machine learning model experiments. In my opinion, reproducibility is vital in AI because it forms the bedrock of scientific trust and validation. It allows for the independent verification of results, facilitating the correction of errors and building upon research findings. Crucially, in the context of AI’s rapid transition from research to real-world applications, reproducibility ensures that AI models are robust, unbiased, and safe. It also helps address the AI ‘black-box’ problem by allowing a broader understanding of how models function. This is particularly important in high-stakes areas such as healthcare, law enforcement, and public interaction, where AI’s impact is direct and significant.
- Datagen: Datagen specializes in computer vision and facial data, ensuring their datasets are varied in terms of skin tones, hairstyles, genders, and angles to reduce bias in facial recognition technology (https://datagen.tech/).
- Galileo and Snorkel AI: Galileo and Snorkel AI focus on maintaining high data quality; Galileo does this by automatically adjusting biases in unstructured data, whereas Snorkel AI ensures equitable, automated labeling, along with data versioning and audit services (https://www. rungalileo.io/,https://snorkel.ai/).
The preceding list is not exhaustive. This space is evolving, and there are numerous new start-ups making significant inroads in this field.
Figure 9.7 – Start-up ecosystem in RAI
The preceding figure, referenced from BGV (https://benhamouglobalventures.com/ ai-ethics-boom-150-ethical-ai-startups-industry-trends/), shows a few notable start-ups providing ethical AI services across five categories: data privacy, AI monitoring and observability, AI audits, governance, risk, compliance, targeted AI solutions and technologies, and open source solution.
Summary
To summarize, the development of more sophisticated AI systems and the journey towards achieving artificial general intelligence (AGI) necessitates a steadfast commitment to RAI principles. Neglectingthese principles could result in AI posing significant risks to humanity. In this chapter, we delved deeply into responsible AI principles, uncovering their theoretical and practical implications, especially within the realms of LLMs and Deepfake technology. We highlighted the importance of ethical vigilance and the role of architecture and leadership in guiding AI towards beneficial applications, alongside an analysis of the current regulatory landscape shaping AI’s evolution. Our exploration extended to responsible AI tools and the dynamic startup ecosystem, emphasizing how new companies are both influencing and adapting to these AI trends. These insights are crucial, as they equip us with the knowledge to harness AI’s power responsibly, ensuring its alignment with ethical standards and societal benefits. Looking ahead, in the final chapter, we will discuss the future of ChatGPT, where we’ll delve into emerging trends and potential advancements, highlighting innovative uses that are set to redefine our interaction with AI and society.
