eDiscovery, legal research and legal memo creation - ready to be sent to your counterparty? Get it done in a heartbeat with AI. (Get started for free)
7 Critical AI Contract Elements Often Overlooked in Due Diligence Checklists
7 Critical AI Contract Elements Often Overlooked in Due Diligence Checklists - Model Bias Assessment Requirements and Testing Protocols
Evaluating potential biases within AI models is becoming a crucial part of their entire lifespan, from initial design to ongoing use. However, due to the wide range of contexts and how AI is used, creating universal fairness standards is a complex challenge. This means we need a more nuanced approach. New models are being developed to address this, emphasizing a collaborative approach that includes the perspectives of people who will be affected by the AI. These models, often structured as a multi-stage framework, attempt to encompass the entire process, from how data is collected to the final evaluation. The field of AI bias evaluation is still evolving, constantly highlighting that bias is a product of the situation and can appear in many different areas of use, potentially causing harm. Because of this, it's absolutely critical to make fairness a core part of how AI is built and managed. Beyond just technical requirements, ensuring AI systems are free of bias is fundamentally important for building trust and acceptance in AI.
Examining the potential for bias within AI models is a critical step throughout their entire lifespan, from their initial conceptualization to their eventual deployment and use. However, establishing universally accepted standards for assessing fairness in AI remains challenging. This difficulty arises from the wide range of contexts, application cases, and the varying types of AI systems being developed. The National Institute of Standards and Technology (NIST) has put forward a framework designed to decrease the risk of bias in AI. A key element of this approach involves the crucial role of collecting input and guidance from individuals and groups who will be impacted by these systems.
A promising approach to testing and evaluating AI systems for bias is a sociotechnical one. This type of approach incorporates a more holistic consideration of the societal context and human interaction in relation to how these models function. It encompasses not only the verification of the model's code and technical design, but also the overall validation process ensuring alignment with ethical and social goals. Researchers have proposed a multi-layered model for standardizing AI fairness assessments, attempting to define a more structured pathway for evaluating these systems. This model, which incorporates stages from initial requirements to post-deployment monitoring, is still a work-in-progress.
The issue of bias in AI is inherently contextual; it's something that appears across a broad range of AI applications. Unfortunately, this can and does have real-world negative consequences. The growing body of academic research focused on fairness in AI can be both a help and a challenge. While researchers now have a broader range of studies to draw on, the growing complexity of this field makes navigating all this knowledge more demanding.
It's worth emphasizing that managing bias in AI systems is not just a technical problem. It's directly connected to fostering and maintaining public trust in these technologies. A comprehensive framework for assessing model bias needs to encompass the entirety of an AI's development journey. This means considering bias throughout the entire cycle, not just at one specific stage. In the quest for responsible AI development, many policy initiatives and best practices are being proposed, though there is still a long way to go before a coherent, unifying approach emerges. The goal is to bring clarity and structure to the practical implementation of these fairness principles.
7 Critical AI Contract Elements Often Overlooked in Due Diligence Checklists - Data Privacy Safeguards Beyond Standard GDPR Compliance
The landscape of data privacy is shifting rapidly in the age of AI, particularly with the emergence of generative AI. While GDPR offers a foundational layer of protection, simply complying with its basic tenets is insufficient. The unique ways AI systems handle data introduce new and complex challenges, demanding a more comprehensive approach to safeguards. Organizations leveraging AI for data processing must go beyond the basics, establishing a robust data governance structure that spans the entirety of the AI's lifecycle. This requires prioritizing transparency and accountability throughout the process, from how data is gathered to how the AI is used. A key aspect is seamlessly integrating AI development and use with broader privacy risk management practices, aiming for a proactive approach to identify and mitigate potential threats. Beyond fulfilling regulatory mandates, these advanced data privacy measures are essential for building public trust and upholding ethical standards within the field of AI. This is crucial not only for AI developers but also for the people and organizations whose data is being used by AI systems.
The rapid evolution of AI, particularly generative AI, has spurred a lot of discussion about data control and individual privacy. It's becoming clear that we need a comprehensive approach to privacy, not just a bunch of disconnected regulations.
Organizations that use personal data in AI need to have a solid reason for doing so and must absolutely follow GDPR rules. This includes respecting people's rights over their own data and keeping that data accurate and up-to-date.
France's data protection authority has highlighted that the different stages of an AI system, like its learning phase and when it's actually being used, need different justifications for why it's using data.
To make sure personal information is protected throughout its entire lifespan within an AI system, we need transparency and accountability in the entire chain of how the data is handled. This is key.
We need to think of AI and privacy risks together. Building frameworks that manage both together can help us understand and deal with the dangers that can come up when creating and using AI.
A good privacy-focused AI system would build trust, strictly follow rules like GDPR, and actively deal with things like data security and the issue of bias in algorithms. It's a big challenge.
It would be beneficial if policy-makers could improve how we make and manage personal data to provide even better protection of privacy.
Discussions at the OECD's AI2S2 Symposium highlighted the need to find solutions to AI's privacy problems. It emphasized the need for AI and privacy specialists to work together, across different areas of expertise.
To make sure AI systems meet the laws in various places, we need accountability systems that match current data protection laws.
When giving guidance about complying with AI, it's vital to include both ethical rules and regulatory requirements. That way, we can encourage people to use AI responsibly and protect data. It's a delicate balancing act.
7 Critical AI Contract Elements Often Overlooked in Due Diligence Checklists - AI Performance Degradation Monitoring Guidelines
Ensuring the continued effectiveness of AI systems over time is increasingly critical. The concept of "AI Performance Degradation Monitoring Guidelines" acknowledges that AI models don't simply perform at a constant level. As the world changes, and the data AI systems rely on shifts, their performance can degrade. This can stem from a number of sources, such as changes in the nature of the data (what's known as data drift) or simply the passage of time, leading to a phenomenon called model "aging." To address this, rigorous monitoring of AI performance becomes essential.
The guidelines stress the need for ongoing assessment of how well AI systems perform, comparing their results against established benchmarks. This allows for the identification of any declines in performance and allows developers and users to address any issues that arise. It also highlights the importance of thoroughly understanding the data that powers AI models. The quality and diversity of this data are critical determinants of how well the AI performs.
Beyond just evaluating performance, the guidelines emphasize the need for a comprehensive approach. This includes building systems that monitor compliance with regulations and assess the ethical implications of using the AI system. All of this serves to mitigate the risks associated with performance degradation and builds greater confidence in AI's overall utility. Without robust monitoring guidelines, AI's potential for harm increases as the systems degrade over time.
Keeping AI systems running well is a constant challenge, especially as the world around them changes. It's like trying to keep a complex machine running smoothly in a dynamic environment. It can be difficult to know exactly when an AI system starts to decline, as it can happen slowly and subtly. This means we need ongoing checks to find problems early on, before they have a major impact.
If an AI model is trained on one type of data but then encounters something very different, it can really struggle. This emphasizes the need for it to adapt to new situations. Imagine trying to use a map from the 19th century to navigate a modern city - it wouldn't be very helpful. AI is similar; if the context changes, its effectiveness might drastically decline.
One common problem that causes AI models to become less effective is something called data drift. Basically, it's when the type of data the AI is fed starts to change over time. This can lead to a silent decline in the AI's accuracy without obvious warning signs. We need to be able to spot this and adjust for it proactively.
Getting real-world feedback from people using the AI system is extremely important. How users interact with it can strongly affect its accuracy and relevance over time. If we don't pay attention to what users are doing, the AI may become less and less helpful as it drifts away from real usage patterns.
Sometimes we make changes to the AI model, but it takes a while to see if those changes have actually improved performance. This period of uncertainty can create the illusion that everything is fine, when it might not be. So we need strategies that help us anticipate potential problems instead of only looking at past data.
It can be helpful to look at the individual parts of an AI model when trying to understand why it's declining. Just like a doctor would try to figure out which organ is causing a problem, analyzing different sections of an AI model can reveal where the issues are and allow for more targeted fixes.
It's important to remember that AI performance problems can have real costs. Especially in fields that rely heavily on AI for making decisions, poor performance can quickly turn into major financial losses. Monitoring should include some assessment of the financial risks involved and the tradeoffs of making changes to fix things.
When you have multiple AI models working together, a problem in one can spread and affect the whole system. Imagine a chain of dominoes - when one falls, others are likely to fall too. We need to monitor how the AI models are interacting with each other, not just their individual performance.
Traditional ways of measuring AI performance might not always be sensitive enough to pick up subtle forms of decline. We might need more sophisticated methods that can detect more nuanced changes. This is a research area in its own right.
Making sure the AI is working well isn't just a technological issue. It also involves the people and organization who are managing the AI. We need to make sure everyone involved is aware of what's going on and prepared to take action if there are problems. It's a shared responsibility between the technical side and those making the broader decisions.
7 Critical AI Contract Elements Often Overlooked in Due Diligence Checklists - Third Party Model Integration Dependencies
Integrating AI models from third parties is becoming increasingly complex and critical for organizations. The sophistication of AI systems, especially generative AI, is leading to a need for much more detailed risk evaluation and management. Businesses that are incorporating these AI models need to refine their due diligence to address the specific challenges, which include things like protecting intellectual property and safeguarding data privacy. These differ from how you would deal with more traditional software integrations. Also, it's becoming obvious that the standard ways of managing third-party risks aren't always good enough for how quickly AI is advancing and the new risks that are popping up. This means we need a broader and more comprehensive approach. The challenge in moving forward with these integrations is balancing the need to be efficient with the need to make sure they are ethical. This calls for constant monitoring and assessment to limit risks as effectively as possible.
When integrating complex AI models, like large language models or generative AI, from external vendors, we find ourselves in a world of interconnected dependencies. These models often have many parts that rely on each other, and a small change in one part can easily spread like a ripple through the whole system. Understanding these relationships is critical, but it can be incredibly tricky due to the intricate nature of modern AI.
Many times, organizations don't fully grasp just how complex these vendor-provided models are or how much oversight they need. This lack of understanding can lead to unexpected issues, as the interlinked web of parts and components can be tough to untangle. We've seen cases where systems that work perfectly when tested alone can act very differently when combined with other models. Sometimes, this happens because the models have conflicting ways of dealing with data or underlying assumptions that clash when used together.
Another point to keep in mind is that integrating third-party models can mean a change in how we're obliged to comply with rules and regulations. This is especially true if the models come from different sources that have different guidelines. It can increase the workload when we're doing due diligence, and it potentially brings in new legal risks that we need to think carefully about.
The rapid pace of change in AI means that third-party models are often updated very quickly. However, this fast-paced update cycle can cause problems if organizations aren't careful. We often see that internal processes for controlling these versions are not well-developed, and this lack of control can lead to problems with system performance and overall reliability if different versions of the models are accidentally used at the same time.
Hidden costs can be a huge problem when dealing with outside vendors and their models. Many organizations don't think about the ongoing support, updates, and training needed for staff to manage these models effectively. This can catch them off guard and end up hurting their budget. We also notice a delay between when a vendor releases a new model and when a company can use it in its own systems. In a quick-changing market, this delay can hurt companies’ ability to compete and manage their operations efficiently.
It's also important to remember that a lot of these third-party models are very sensitive to the kind of data they are exposed to during integration. If there are problems with the quality of the data from another part of the system, it can lead to unintended results and reduce trust in the AI system. Many times, companies find that it's tough to modify these models to fit their specific requirements, which means they need to do extensive changes to their internal ways of working or accept that the model might not perform optimally.
A final challenge worth highlighting is that using third-party models can, without us realizing it, spread biases from the data the models were built on. This is especially a concern if the data reflects social inequities. Companies need to very carefully study these issues and address them before putting the models into use to ensure fairness. It's a complex issue but one we can't ignore.
7 Critical AI Contract Elements Often Overlooked in Due Diligence Checklists - AI System Retraining Cost Distribution
When assessing AI contracts, it's easy to overlook the ongoing costs associated with retraining AI systems. However, understanding how these costs will be distributed is crucial. The scale and intricacy of the AI model, along with the frequency of retraining, can significantly impact these costs. Choosing between regularly scheduled retraining or performance-based updates can also have a notable effect on the budget and potentially disrupt operations. It's important for contracts to explicitly lay out who's responsible for retraining and what expectations are in place. This includes clarifying how both the financial and operational effects of retraining are addressed. Failing to address these aspects could not only strain budgets but could also negatively impact the AI system's long-term usefulness and dependability.
### AI System Retraining Cost Distribution: A Closer Look
The process of keeping AI systems up-to-date often involves retraining, and it's a recurring cost that's easy to overlook. Many AI models need to be retrained at least every six months to stay accurate, particularly if the data they're using is changing frequently. This constant retraining adds up to a significant expense over time, and it's something organizations frequently don't factor into their initial budgeting.
One aspect that's often underestimated is the expense of data collection and preparation. This cost can surprise organizations, sometimes reaching up to 70% of the total retraining budget. The actual computational costs associated with the retraining itself can sometimes seem less impactful compared to how much it takes to prepare the data the AI will learn from.
The type of infrastructure used plays a large role in determining the cost of retraining. Organizations that rely on cloud computing services typically experience much more variable expenses compared to those using in-house data centers, since cloud providers often charge based on how much the system is being used. This can lead to unexpected increases in expenses as retraining needs fluctuate.
Another surprising issue is that retraining costs can get much higher as AI systems grow in size and complexity. Adding more data to a system doesn't just require a more powerful computer to handle the calculations. It also means the model needs to be carefully fine-tuned again, leading to a much more complicated and costly process.
It's not unusual for organizations working with sophisticated AI systems to find themselves needing specialists to manage the model retraining. This skill shortage within the workforce often results in higher costs related to either hiring experts or training existing employees. These costs associated with retraining can be substantial.
Retraining can also take place in different ways. Real-time retraining, which is designed to adjust to changes quickly, is often much more expensive than batch retraining, where adjustments happen at set intervals. Real-time models constantly need computing resources, leading to more consistent spending that might be difficult to predict.
The quality of the data used during retraining plays a big role in the overall cost. High-quality, structured data generally makes the retraining process smoother and requires less preprocessing, reducing expenses. On the other hand, poor quality data usually results in extra effort and multiple rounds of retraining before a satisfactory outcome is reached.
Regulations can also introduce additional costs associated with retraining. If AI models must comply with privacy laws or fairness guidelines, they may require more frequent review and adjustment. This additional oversight during the retraining process can quickly increase the overall expense.
AI models can also decline in effectiveness over time due to changes in the data they're designed to work with. This is sometimes called data drift, and it leads to a kind of 'obsolescence' for the model. The costs of retraining to make sure a model remains accurate can be significant and are often not included in initial budget projections.
Finally, retraining costs are often spread across different parts of an organization. Some departments might be able to share resources and data, lowering their individual expenses. But other departments might face higher costs that can be difficult to predict or manage within the overall budget. This creates complex budgeting challenges as companies try to understand the cost of maintaining their AI systems.
It's easy to see that simply building an AI model isn't the end of the financial journey. The ongoing cost of keeping those systems functional, up-to-date, and compliant with evolving standards adds a whole new dimension to the overall expense of using AI. Researchers and engineers need to be aware of the hidden costs that come with retraining to create a more realistic picture of AI's true price tag.
7 Critical AI Contract Elements Often Overlooked in Due Diligence Checklists - AI Output Explainability Standards
The increasing sophistication of AI systems has brought about a greater need for standards that ensure the explainability of their outputs. These standards are crucial for fostering trust in AI and holding developers accountable for its decisions. It's no longer enough to simply have AI systems that function; we must also be able to understand how they arrive at their conclusions. This isn't just a technical concern, but one that touches on broader societal questions about how we interact with and rely on AI in our daily lives.
There's a growing focus on developing methods that allow humans to better supervise AI systems. This involves establishing ethical guidelines that address the potential social impact of these technologies, and developing clearer ways to communicate how AI outputs are created to users and the broader public. The aim is to make AI more accessible and comprehensible, encouraging greater understanding of how these powerful tools work. As AI's influence expands across many industries, these explainability standards become increasingly important for maintaining compliance with evolving regulations and ensuring responsible AI development. Companies that fail to integrate these concepts into their operations and contracts could potentially face significant risks.
The pursuit of AI explainability is driven by the increasing intricacy of machine learning models, making it harder to retrace how decisions are made. While transparency and explainability are closely linked, transparency focuses on the 'what' of an AI system (what happened), whereas explainability dives into the 'how' (how the decision was reached). The public's growing interest in understanding AI's inner workings has spurred efforts to improve human oversight of these systems. This has led to a surge in research focused on developing explainable AI techniques, aiming to bolster transparency, enhance user control, and foster a deeper understanding of AI's outputs.
However, fostering trust in AI goes beyond simply providing explanations. Explainable AI necessitates navigating social and technical aspects that contribute to trustworthiness. The National Institute of Standards and Technology (NIST) has outlined a set of principles for trustworthy AI, highlighting the need for standardized evaluation, verification, and validation of AI models. These principles mirror the call for transparency and explainability that are becoming increasingly important quality criteria, particularly in the context of machine learning, as highlighted by ethical guidelines for AI.
The widespread adoption of AI across various sectors has ignited concerns regarding the lack of transparency in AI decision-making. The repercussions of AI explainability are multi-faceted, influencing aspects like user expectations, cultural norms, legal requirements, and organizational values. The growing volume of academic work dedicated to transparency and explainability demonstrates a growing acknowledgment of their pivotal roles in AI system design and implementation.
Yet, despite this focus, standardization in AI output explainability is still elusive. There's no universal agreement on what constitutes a good explanation, leading to inconsistency in how these guidelines are interpreted and applied across different organizations. Even with AI providing explanations, humans vary widely in their interpretations. This inconsistency can diminish confidence in AI systems and potentially lead to misinterpretations of the same output explanation.
Furthermore, not all AI explanations are necessarily suitable for all users. Highly technical explanations, while helpful for developers, might be confusing for ordinary users. This highlights the need for tailored explanations, matched to the user's background and expertise. Furthermore, implementing explainability standards can considerably extend the development process, as creating AI models with transparent decision-making mechanisms often introduces additional complexity, potentially impacting project schedules and the allocation of resources.
This is further complicated by the inherent difficulty in explaining complex AI models, like deep learning networks. Their sophistication can make it hard to decipher their outputs in a meaningful way. This creates a challenge: improving the power of AI can hinder our ability to understand it. Adding to this complexity, different industries are developing their own explainability standards, shaped by their specific regulatory frameworks and ethical considerations. This can create a patchwork of regulations that complicates the use of AI across different sectors.
Regulations are also playing a crucial role in demanding explainability. As scrutiny around AI decision-making intensifies, organizations are incentivized to prioritize explanations, as a lack of clarity could result in legal consequences, particularly in sensitive sectors like finance and human resources. However, feedback mechanisms following AI outputs are often inadequate, hindering efforts to improve explainability approaches. Without clear avenues for users to express how well an explanation elucidated an AI's decision, it becomes challenging to iteratively refine explanation methodologies.
Moreover, organizations frequently underestimate the resources required to effectively put explainability measures into practice. From specialized personnel to software that promotes interpretability, this can lead to underfunded projects. Furthermore, ensuring AI explainability is not solely a technical endeavor; it intersects with ethical dimensions of accountability, especially considering the possibility of AI systems inadvertently perpetuating biases present in training data. Clear and insightful explanations can help to identify these biases. However, if comprehensible explanations aren't available, it can lead to significant ethical concerns.
7 Critical AI Contract Elements Often Overlooked in Due Diligence Checklists - Model Version Control and Update Management
When it comes to AI, ensuring that models remain effective and reliable over time is crucial. This involves carefully managing model versions and updates. Having a robust system for tracking changes and releases is key for collaboration within development teams. This kind of system can prevent confusion, facilitate smooth updates, and help keep development moving without major disruptions.
It's also important to acknowledge that AI models can gradually lose effectiveness as the data they work with shifts or as the model itself 'ages'. This means having a good system for monitoring performance and implementing updates is necessary. Detailed protocols to address model drift, data changes, and regular assessments can be woven into the management framework.
Sophisticated model registries can significantly enhance model version control and update management. These tools not only streamline the process of handling updates but also provide a security layer by ensuring that only authorized individuals can alter or release new model versions. This security aspect is increasingly important as the use of AI expands into more sensitive areas.
Considering that the field of AI is rapidly evolving, it is vital for organizations to include these practices in their contracts. This helps mitigate risks, improves clarity on who is responsible for updating models, and contributes to the long-term success and trustworthiness of the AI system. Simply put, neglecting a clear structure for managing updates could have major negative implications for the functioning and reliability of an AI model.
Model version control and update management are surprisingly complex, especially as AI models become increasingly intricate. Think of it like this: as the number of layers in a neural network goes up, the challenge of keeping track of different model versions explodes. Every new tweak or improvement requires careful monitoring to ensure it doesn't accidentally make the model worse or introduce unwanted biases.
Research shows that even the most carefully crafted models can start to perform poorly within a few weeks of being put into use due to 'data drift', meaning the real-world data the AI is dealing with changes over time. This implies that we need to update models quite frequently, which can quickly become overwhelming for development teams if there's no systematic way to manage all those updates.
Things get even more complicated when you integrate models from other companies. Often these models depend on a bunch of external software libraries or services, which can be thought of as a tangled web of dependencies. If one part of that system is updated, it can trigger a chain reaction across the whole AI system in unexpected ways, which can be very hard to debug and fix.
Keeping models up-to-date in real-time systems also introduces the challenge of increased delays. This is particularly problematic in situations where split-second decisions are essential, like in self-driving cars or stock trading applications.
Effective version control depends heavily on a detailed record of a model's past performance. However, surprisingly many companies don't keep track of all that information, which means they miss out on potentially valuable insights when they go to make changes in the future. It also makes troubleshooting difficult.
Managing all the different model versions can also add significant costs that frequently exceed the original development budgets. This includes things like the increased use of computers and the expertise required to carefully test every new version before it's used in real situations.
It's also worth noting that the constant push to update AI models can lead to 'change fatigue' within the teams that are responsible for maintaining these versions. This can result in resistance to updates that are really necessary, which can impact how effectively the AI actually functions.
Furthermore, companies tend to underestimate how fast a model can become out-of-date due to changes in user habits or broader trends. Academic studies show that AI models typically need a 'tune-up' every couple of months to remain relevant to the world around them, yet regular updates are rarely made a high priority.
Another issue is that there isn't a standard way of testing updated AI models, which leads to a lot of companies creating their own testing systems. This inconsistency can lead to unreliable performance across various applications and contexts.
Finally, in fields like finance or healthcare where rules and regulations are very important, AI model updates can easily lead to breaking those laws if the changes are not closely scrutinized. This can result in large fines for companies, further highlighting the importance of rigorously managing model versions.
All these challenges highlight the often-overlooked need to think carefully about how we maintain and update AI models, which can be very tricky given how fast this area of technology is moving.
eDiscovery, legal research and legal memo creation - ready to be sent to your counterparty? Get it done in a heartbeat with AI. (Get started for free)
More Posts from legalpdf.io: