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Category: analysis

  • ⚡️🛫 RAG in Legal Tech: How Retrieval-Augmented Generation is Transforming Law

    ⚡️🛫 RAG in Legal Tech: How Retrieval-Augmented Generation is Transforming Law

    📂 Remember the days spent sifting through endless files or scouring outdated databases for that one crucial piece of information? Whether you’re a 💼 lawyer, a 🔬 researcher, a 📈 marketer, or any professional who relies on knowledge, we’ve all been there. 👋 Say goodbye to those frustratingly slow and inefficient workflows.

    🚀 The game-changer is here: Retrieval-Augmented Generation (RAG). This cutting-edge AI technology is set to revolutionise how we work across various fields, giving us instant access to up-to-date and relevant knowledge right at our fingertips. It’s like strapping a jet engine onto your trusty old bicycle—suddenly, you’re not just keeping up, you’re soaring ahead of the competition. In this post, we’ll dive deep into how RAG is transforming the legal landscape, but its potential reaches far beyond.

    What is RAG, and Why Should You Care?

    In simple terms, Retrieval-Augmented Generation (RAG) lets AI access a vast, up-to-the-minute pool of knowledge while answering your questions. No more relying on outdated datasets! This means your legal insights are not only accurate but also reflect the latest developments—a game-changer in our fast-paced legal world.

    RAG in Action: Real-World Examples

    • Compliance, Simplified: Ever spent hours deciphering the latest GDPR updates? With RAG, you can ask, “What’s new with GDPR?” and get an instant, digestible summary. No more scrambling to keep up with regulations – RAG’s got your back. See our example here.
    • Legal Research, Supercharged: Forget trawling through endless case law databases. Specialised legal AI tools like Harvey AI and CoCounsel AI, powered by RAG, let you pinpoint the most relevant precedents with pinpoint accuracy.
    • Strategy, Not Just Reaction: RAG isn’t just about keeping up; it’s about getting ahead. Use it to spot emerging trends, anticipate legal challenges, and craft proactive strategies that position your clients for success.

    The Fine Print: RAG’s Limitations

    Of course, even the most cutting-edge tech has its limits. RAG is no exception:

    • Knowledge Cut-Off: AI models have a ‘knowledge horizon’ and may miss the absolute latest developments, even with RAG. Always double-check crucial details.
    • Model Quirks: AI isn’t perfect; it can have blind spots or biases. Use your own expertise to interpret and apply its insights.

    The Ethical Imperative: Using RAG Responsibly

    As AI becomes more integrated into our work, human oversight and ethical considerations grow in importance. In the legal world, where upholding justice and protecting client interests are paramount, it’s crucial to ensure that AI is used responsibly and ethically. RAG can be a powerful ally, but it’s up to us to wield it with wisdom and integrity.

    ☘️ The Future is Bright (and RAG-Powered)

    RAG is already transforming the way we work, but the best is yet to come. Imagine AI tools that seamlessly integrate with your organisation’s knowledge base, predict industry trends, and even draft customised reports or documents. The possibilities are endless.

    So, are you ready to embrace the future? Dive into the world of RAG and discover how it can elevate your work to new heights. From streamlining compliance to supercharging legal research and enabling proactive strategies, RAG is poised to redefine how we access and leverage knowledge. Just remember, while RAG is a powerful tool, it’s at its best when combined with human expertise and ethical considerations.

    Continue exploring practical AI

    Retrieval-augmented generation is one component of useful AI systems. The wider questions include how to configure AI assistants, which tools to trust and how to keep humans accountable for decisions.

  • AI Waves: Is your business ready for the new EU AI Regulations?

    AI Waves: Is your business ready for the new EU AI Regulations?

    AI Regulations by Geofrey Banzi

    AI Waves has returned once more! The most recent instalment of this series of webinars focuses on the proposal for a regulation of the European Parliament and of the Council imposing harmonised rules on AI.

    Questions Covered:

    • Is it possible to create a precise definition of AI?
    • Is the description provided by the EU proper and relevant to its practical use?
    • How much does the quality of data affect AI algorithms?
    • Can the data quality be regulated and how could it be done?
    • How can you locate a high-risk algorithm? Does the European Union correctly describe current AI threats? Are the new regulations necessary?
    • How can the new laws affect business?

    Speakers:

    Jerzy Biernacki (Miquido), Lukasz Wieczorek (Konieczny, Wierzbicki Law Firm), Michal Barwicki (Konieczny, Wierzbicki Law Firm), Beril Sirmacek (Saxion University of Applied Sciences) and Martin Ciupa (Serial AI Entrepreneur)

    Key Takeaways:

    • Financial markets are currently leading the way regarding the regulation of Artificial Intelligence.
    • A fundamental issue for AI regulation is the determination of appropriate definitions of AI and high-quality data.
    • The risk-based method used in the Act to differentiate between different AI applications and products needs further specification.
    • The implementation of the Act will likely have large scale impacts on the competitiveness of EU economy and business due to the widespread involvement of AI across all sectors.

    Overview of updates to the EU regulations

    The Artificial Intelligence Act is an act that has been proposed by the European Commission and is therefore not yet binding.

    Regarding the implementation of the regulation, it is stated that each Member State will appoint an AI authority alongside the appointment of a European Artificial Intelligence Board as a coordination/assisting authority.

    Points of significance:

    • Defining Artificial Intelligence
      • The term ‘Artificial intelligence system’ is defined in the proposed Act as:

    Software that is developed with one or more of the techniques and approaches listed in Annex I and can, for a given set of human-defined objectives, generate outputs such as predictions, recommendations, or decisions influencing the environments they interact with. 

    • Potential Issues from a Business Perspective
      • Too narrow or too extensive
      • What methods will be applied to ensure the periodic updates are effective
      • Interpretation divergence risk between EU Member States
    • Risk Based Classification
      • Simply put, the method used to deduce the amount of regulation that will be applied is determined based on the amount of risk that the use of Artificial Intelligence poses to the user.
      • Four categories of risk are identified:
        • Unacceptable risk
          • Includes prohibited practices, e.g., manipulating people and social scoring.
        • High risk (area of specific intrigue for lawyers and those in business)
          • Includes uses that carry very significant risk to security, rights, and health of users, e.g., safety components of products, critical infrastructure, hiring and workforce management and the prosecution of crime
          • The providers of these AI applications and products must meet strict requirements because of the impact that it may have on the course of life
            • Risk assessment and mitigation
              • Concern – Not clearly defined.
            • Use of high-quality data sets
              • Concern – What is meant by this? 100% free, 98% etc. This requirement seems impossible to guarantee.
            • High level of accuracy, security, and robustness
              • Concern – This will require constant modifications.
              • Concern – Accuracy is a terrible metric for AI
            • The general sentiment is that an intermediate risk category is needed between the high risk and limited risk classifications.
        • Limited risk
          • E.g., chat bots
        • Minimal risk
          • E.g., AI computer games
    • Penalties
      • Up to 30 000 000 EUR
      • The numbers are big and therefore the Artificial Intelligence Act is something that business must pay close attention to avoid huge sanctions when the act becomes legally binding (resembles the impact that GDPR had on business)
    • Additional comments and concerns
      • Liability repatriation vis-à-vis the user
        • Interesting to business entities – may need to develop a way to share liability between the component AI subsystems involved in the final applications and products.
        • Currently, regulation says the provider will be liable.
      • AI system user impact on system’s performance
        • Should we share some liability, requirements, obligations with the user?
      • Testing of algorithms
        • Should we be focusing on testing over asking for complete knowledge of the exact function of the AI? The level complexity seems to be pointing this way (an appropriate comparison would be practices in medicine, namely the passing of new drugs and vaccines)
      • Adding another AI system category
        • Intermediate

    Panel Discussion

    • The definition of artificial intelligence
      • This is an exceptionally complicated task, and it is impossible to know how such a definition will materialise in its application.
      • It will be very interesting to see what the different stakeholders think of the current approach and the extent of the potential disagreements which may arise.
    • The role of quality data in working on AI algorithms
      • This is an area of central importance for the Act. A more precise definition of what this means needs to be put forward and ‘high quality data’ is interpreted by experts in AI and business.
    • The potential risks of AI and keeping control over the technology
      • The general sentiment across the board is that regulation is necessary, however, that the act is far from being ready.
      • Inevitably, this regulation will impact the competitiveness of EU economy and business – this needs to be taken into consideration (from business perspective)
    For more on Miquido, please visit here.

    What followed the early AI regulatory debate?

    This recording discusses a proposed regulatory approach at a particular point in time. For wider context about fairness, institutional accountability and machine authority, follow the related research.

  • DOES YOUR AI PASS THE EUROPEAN COMMISSION RISK-ASSESSMENT FRAMEWORK?

    DOES YOUR AI PASS THE EUROPEAN COMMISSION RISK-ASSESSMENT FRAMEWORK?

    THE EUROPEAN COMMISSION RISK-ASSESSMENT – SAFEGUARDING KEY HUMAN RIGHTS AND FREEDOMS.

    The European commission has delivered a new framework for trustworthy Artificial Intelligence (AI) that can be accessed online. The new Risk-Assessment Framework importantly puts a practical spin on the Commissions earlier previously published guidelines . This brief covers the main aspects of the European Commission’s framework, outlining its key features whilst also bringing in some analysis from recent developments.

    In developing the new Risk-Assessment Framework, the European Commission have emphasised the importance of observing an AI’s overall impact on core human rights. The main elements of the Risk-Assessment framework explores this further by covering core rights, such as those relating to non-discrimination, protection of personal data and freedom of expression.

    The main areas covered by the new Risk-Assessment framework include:

    (A) Human Agency and oversight
    (B) Technical Robustness and Safety
    (C) Privacy and Data Governance
    (D) Transparency
    (E) Diversity
    (F) Societal and Environmental well being
    (G) Accountability

    EXAMINING THE EUROPEAN COMMISSION FRAMEWORK

    (B) Human Agency and oversight

    This part of the Risk-Assessment Framework covers whether the system might create over-reliance in users, or illegitimately manipulate users’ behaviour. Whilst AI won’t be capable of making ‘human’ errors, it will however still be fallible to both manufacturing and design errors, as well as ‘machine errors’, fuelled by the algorithmic system itself. This presents clear problems in areas where AI, might be used to substitute human agency in its entirety. An example of this being, stage 5-6 of self-driving cars, where the AI algorithm is said to have complete control without driver interference (for more information see SAEs 5 Stages of Automation and our previous post on self-driving car fundamentals).

    (A) Technical Robustness and Safety

    This covers the subject of security, including resilience to cyber-attacks, general safety as well as the potentially devastating effects of inaccuracy and unreliability.

    (C) Privacy and Data Governance

    This aspect questions the impact of the AI system on the rights to privacy, the right to physical, mental or moral integrity and the right to data protection, and what mechanisms are in place to minimise potential harm and deal with concerns. 

    (C) Transparency

    This covers the traceability of decisions and how, why and where data has been used. An issue with this aspect arises when looking at self-driving car “Black-boxes”, which generate specialist outputs or decisions that cannot readily be explained to users.

    (D) Diversity, Non-discrimination and fairness (AI ETHICS)

    This part of the Risk-Assessment Framework is particularly concerned with the AI designers of today, who have been spearheading recent developments. Recent events have highlighted the importance of avoiding discrimination of all forms more than ever and even more so in the context of algorithmic bias. This was recently highlighted in a recent Ted talk by the president of the Law Society ‘The Future is Biased’ , Christina Blacklwas.

    AI systems should avoid unfair bias and its important that designers not only have a diverse end user in mind, but as an employment group, are also adequately represented by a diverse class of people. Ultimately, the end system should be fair regardless of age, gender, race or other characteristics.

    (E) Societal and Environmental well being

    Naturally, as with the development of any new complex system designed to help humanity, any potential and actual adverse impacts to society and the environment should be limited, as far as possible. More-so, as we are so inextricably linked to both. Whilst its true, that initially adoption of AI in many instances has the potential to impact large areas of employment, leading to job-losses as a result of large-scale automation. This is a foreseeable result that should be accounted for in governmental contingency plans with ready measures to counter any negative effects. Ultimately and most importantly, the overall benefits to society, should outweigh any negative impact predicted to arise.

    (F) Accountability

    Last, but most importantly, its going to be crucial that any AI system developed going forwards has a system of accountability for when unjust or unfair outcomes do occur. This should also ensure an adequate system of redress. In the UK for example, this can thankfully be seen with the ‘Automated and Electric Vehicles Act 2018’ which provides the system of redress for self-driving cars under a single insurance scheme. For more details, see Driverless cars Insurance laws receive Royal assent

    Conclusion

    Now that we have a checklist and Risk-Assessment framework put in place for assessing the suitability of present and future AI systems, what remains to be seen is how developers and employers of AI systems will choose to implement its philosophy going forwards. Nonetheless, it’s certain that the principles are a great first step that works towards helping end users benefit from AI equally, without being exposed to unnecessary risk.

    Stay tuned for more great content

    Continue exploring

    This article reflects an earlier stage of Europe’s AI governance debate. Compare those ideas with today’s questions about delegated authority in our Autonomy and AI research hub, and investigate decision authority in the interactive Autonomy Atlas.