AI Governance IBM principles For Businesses And Cities

The Watsonx.governance model by IBM is an Enterprise AI tool meticulously designed to guarantee responsible, ethical, and effective utilisation. This tool is crafted to align seamlessly with both business objectives and societal norms.

The period from 2013 to 2022 set the stage for the current state of AI, laying the technological foundation, raising awareness about ethical considerations, and integrating AI into the fabric of business and society. The legacy of this era is a robust, dynamic field that continues to evolve and shape various aspects of our world.

This is before the Generative AI revolution. 2023 changed and is still revolutionising all things with Generative AI leapfrogging the AI landscape in a dangerous (even if exciting) path to AGI.

So where do we stay now when it comes to AI IP and patents? What will happen with the patents and specially with AI patents in a time of generative AI? As the innovation and disruption goes on, this new ecosystem is very different from anything in the past.

The velocity and the impact took the world by storm and at the moment Microsoft and openAI have a big percentage of the market followed by Google and Amazon.

Generative AI from 2023

Research from iot-analytics.com shows that Generative AI is taking the AI legacy world. While OpenAI, Microsoft, AWS, and Google, took the major market share for AI, IBM was amongst the other major legacy players to redesign a new strategy and adapt to this new world.

Moreover, one important, very important subject is that we are now in a time when it is no longer Humans that can create AI patents, patents can now be created by or with AI. In fact, all creation of AI and tech now is done with some type of generative AI tool. So where do we stay when it comes to creating patents?

When it comes to AI-related intellectual property (IP) and the creation of patents, there are several international considerations to keep in mind. These considerations are shaped by the rapid evolution of AI technology, its cross-border nature, and the diverse legal frameworks across different countries.

Here are the major international considerations:

One key aspect to navigate is the patentability criteria, which encompass factors such as innovation, non-obviousness, and industrial applicability. However, these criteria can vary significantly between jurisdictions, presenting a complex landscape for patent seekers. Additionally, the ongoing debate regarding human vs. AI inventorship adds another layer of complexity. While most jurisdictions currently mandate a human inventor, the evolving nature of this space introduces uncertainty.

Ownership and inventorship issues further complicate the landscape, especially concerning AI-created IP. Questions arise about the naming of AI systems as inventors, ownership rights in the case of employee and contractor inventions, and the necessity for clear agreements and policies.

Disclosure requirements for patent applications involving AI inventions involve providing detailed information on algorithms, training methods, datasets, and more. This poses challenges, especially when businesses opt for protecting their AI innovations as trade secrets to avoid disclosing sensitive information.

Data rights and privacy considerations are paramount, given that AI systems are often trained on large datasets. Adhering to international data protection laws, such as GDPR, becomes crucial to ensure compliance and ethical use of data.

On an international level, harmonization and treaties, such as the Patent Cooperation Treaty (PCT), offer avenues for seeking patent protection globally. However, despite these initiatives, significant differences persist in how AI-related IP is treated across jurisdictions, necessitating a tailored approach for each region.

Enforcement challenges arise in the form of cross-border enforcement difficulties, especially when patent infringement occurs across jurisdictions with varied legal frameworks. The rapid technological evolution in the AI space further complicates matters, rendering patents quickly obsolete and necessitating continuous innovation for businesses to stay ahead.

Ethical and social considerations bring forth debates about the balance between protecting AI innovations and ensuring broad access to AI technology for societal benefit. Issues related to bias and fairness in AI systems' design and training data usage add significant dimensions to discussions surrounding the ethical use and protection of AI technology. As the AI landscape continues to evolve, stakeholders grapple with these multifaceted challenges that demand thoughtful consideration and strategic responses.

AI Governance principles by IBM

IBM is known for its commitment to ethical AI and has established a set of governance principles, which include:

Transparency and Explainability: IBM places a strong emphasis on clear communication regarding the utilisation of AI. Decisions made by AI systems should be explained to foster a deeper understanding of their functioning.

Accountability: IBM firmly believes that, although AI can enhance human intelligence, the ultimate decision-making authority should reside with humans. This approach ensures a clear chain of accountability for the outcomes of AI-driven processes.

Fairness and Bias Mitigation: IBM actively engages in efforts to create and deploy AI systems that are devoid of bias. The goal is to guarantee that these systems treat all users impartially and avoid perpetuating any unfairness or discrimination.

Privacy and Security: Committed to upholding the highest standards, IBM pledges to safeguard data privacy and security. This entails ensuring that user data remains protected, and AI systems are resilient against potential security threats.

By incorporating these principles and practices, businesses have the opportunity to establish a robust governance framework for AI. This framework not only ensures the ethical conduct of AI initiatives but also fosters compliance with regulations while aligning seamlessly with both business objectives and societal values.

Watsonx.governance: The AI centered governance model for AI by IBM

IBM emphasises the significance of an ethical, AI-centred approach to governance. Their framework involves a wide range of stakeholders including AI developers, users, policymakers, and ethicists, ensuring that AI systems align with societal values. This comprehensive involvement is crucial for developing and using AI-related systems responsibly​​.

IBM’s AI Governance Consulting services underscore the transformative potential of AI in business. They offer expertise in helping enterprises leverage AI to drive business transformation and harness the value from AI-induced disruptions. This approach emphasises the strategic integration of AI governance into business practices, ensuring that AI initiatives are aligned with business objectives and deliver substantial value​​​​.

Additionally, IBM’s AI Academy on AI Governance provides resources for setting up responsible AI workflows and outlines the overall process of AI activities in an organisation. This guidance is geared towards ensuring that organisations’ AI initiatives result in trusted outcomes and explainable results, highlighting the importance of transparency and accountability in AI operations​​.

These principles and resources from IBM offer a robust framework for businesses looking to incorporate AI into their operations ethically and effectively, ensuring that AI governance is an integral part of their organisational strategy.

As a new and important strategy that expands the scope of the challenges and opportunities with AI, IBM Consulting is expanding its strategic expertise to help clients / partners adopt responsible AI practices, encompassing automated model governance and broader organizational governance. This includes addressing AI ethics, organizational culture, accountability, training, regulatory compliance, risk management, and cybersecurity threats.

The watsonx.governance offering is part of the IBM watsonx AI and data platform, alongside other products like AI assistants and data storage solutions, designed to assist enterprises in scaling and accelerating their AI initiatives. Additionally, IBM is offering intellectual property protection for its IBM-developed watsonx models.

The broader watsonx portfolio allows IBM aims to enable businesses to innovate with AI while maintaining transparency, accountability, and control over their AI initiatives.

Dinis Guarda

Dinis Guarda is the founder and chief vision architect for citiesabc.com and CEOCreatorAuthor of freedomxcom. He has before created the platforms openbusinesscouncil.org, fashionabc.org, intelligenthq.com, hedgethink.com, tradersdna.com and and IP technologies blocksdna.com, lifesdna.com, iDNA and indexDNA.

With 20+ years experience in international business and digital transformation Dinis Guarda has been a Lecturer and guest Speaker in international business schools such as: Cambridge, Kings College, Copenhagen Business School, INSEEC, Monaco University among others. Dinis is the author of various books. His upcoming book, titled 4IR Magna Carta Cities ABC: A tech AI blockchain 4IR Smart Cities Data Research Charter of Liberties for our humanity is due to be published in 2020. Before that, he has published “4IR AI Blockchain Fintech IoT Reinventing a Nation“, “How Businesses and Governments can Prosper with Fintech, Blockchain and AI?”, also “Blockchain, AI and Crypto Economics – The Next Tsunami?” among others. He was responsbile for over 20 books/ebooks/magazines published in various languages.

Dinis is a serial entrepreneur and CEO / chairman of the companies ztudium / techabc / open business platform. Dinis is involved as a strategist, board member and advisor with the payments, lifestyle, blockchain reward community app Glance technologies, for whom he built the blockchain messaging / payment / loyalty software Blockimpact, the seminal Hyperloop Transportations project, Kora, and blockchain cybersecurity Privus.

He is listed in various global fintech, blockchain, AI, social media industry top lists as an influencer in position top 10/20 within 100 rankings: such as Top People In Blockchain | Cointelegraph and https://cryptoweekly.co/100/ .

Between 2014 and 2015 he was involved in creating a fabbanking.com a digital bank between Asia and Africa as Chief Commercial Officer and Marketing Officer responsible for all legal, tech and business development. Between 2009 and 2010 he was the founder of one of the world first fintech, social trading platforms tradingfloor.com for Saxo Bank. More about him here https://www.openbusinesscouncil.org/wiki/dinis-guarda/