The swift progress of artificial intelligence (AI), particularly generative AI, has ignited the need for robust regulations addressing ethical, transparency, and trust concerns.
In this article, we explore the impact of generative AI on content generation, the role of copyright laws and existing AI regulations, and strategies for translating the principles outlined in these regulatory frameworks into actionable steps.
Impact of Generative AI on Content Generation
Generative AI models, such as ChatGPT, DALL-E, and NotionAI, have transformed content creation by producing vast amounts of high-quality content with minimal human intervention. This has enhanced efficiency and lowered costs for various industries, including marketing, journalism, and creative writing.
However, concerns about potential misuse, including fake news, deepfakes, and the spread of misinformation, have also surfaced.
Achieving a balance between promoting innovation and ensuring responsible AI usage entails comprehending generative models’ capabilities and limitations and formulating guidelines for their ethical application.
The Role of Copyright Laws in AI-Generated Content
The emergence of AI-generated content challenges conventional copyright frameworks, which were designed for human authorship.
Legal systems must evolve to safeguard intellectual property rights while encouraging innovation. Continuous engagement among legal experts, AI developers, and stakeholders is necessary to ensure equitable and adaptive copyright laws.
Several recent rulings have tackled challenges related to AI-generated content and copyright laws. Some jurisdictions have contemplated granting copyright protection to AI-generated works under specific conditions, while others have deemed them ineligible due to the absence of human authorship. Striking the right balance will be vital in determining the future of content creation and reuse.
From Principles to Action: Making Existing AI Regulations Actionable
Various AI regulations, such as the US Federal Reserve SR11-7, the EU AI Act, the NIST AI Risk Management Framework (AI RMF), and Singapore’s AI Act, to name a few, underscore the global interest in managing AI’s ramifications. These regulations are designed to create a foundation for responsible AI practices across the globe.
In order to build on this foundation and cultivate a responsible AI ecosystem, key concerns must be addressed. These include making regulations actionable and measurable, integrating them into the AI lifecycle, and assigning clear roles and responsibilities within organizations. This will ensure a robust framework for AI development and deployment that aligns with the ethical guidelines and standards set forth by these regulatory bodies.
How can we make regulations actionable?
To make regulations actionable for AI, it is crucial to translate high-level regulatory principles into concrete steps. Collaboration between regulators, AI developers, and businesses is essential to devise frameworks that outline requirements and expectations. Regulations must be adaptable and allow organizations to maintain compliance without hampering innovation.
How can we make regulations measurable?
Measurable regulations enable organizations to assess their compliance and demonstrate their dedication to responsible AI development. To achieve this, regulators must establish clear metrics and benchmarks against which organizations can evaluate their AI systems. Creating a standard way to measure AI compliance can lead to a more responsible AI environment.
How can we incorporate regulations into the AI lifecycle workflow?
Integrating regulations into the AI lifecycle workflow is vital for guaranteeing responsible AI development from design to retirement. This process starts with putting regulatory concerns into the AI design phase, so that possible risks and ethical issues with the technology can be planned for.
How can we assign roles and responsibilities within an enterprise?
Setting clear roles and responsibilities within an organization is a key part of putting AI regulations into place. By making it clear who is in charge of each part of AI regulation compliance, organizations can better manage their AI systems and make sure they follow legal and ethical rules.
By addressing these key considerations, we can move closer to achieving a responsible and sustainable AI ecosystem that champions both innovation and adherence to ethical standards.
Konfer is at the forefront of these efforts.
Konfer helps ensure that AI is responsible and trustworthy by focusing on two key aspects: AI visibility and transparency. Konfer ensures that AI-generated results can be understood and traced back to their decision-making mechanisms so that people can trust the AI they are working with.
For AI to be used in a responsible way in the future, we need to find a balance between encouraging innovation and addressing issues of ethics, transparency, and trust.
By making AI regulations practical, quantifiable, and integrated throughout the AI lifecycle, and by defining distinct roles and responsibilities within organizations, we can cultivate a sustainable AI ecosystem that encourages growth while safeguarding against potential misuse.
To learn more about Konfer’s role in promoting responsible AI development and use and how collaborating with Konfer can provide your organization with tailored solutions to ensure ethical, transparent, and trustworthy AI deployments, get in touch with us.
Artificial intelligence and machine learning (AI/ML) follows a cyclical process to help companies acquire practical business value. The traditional AI/ML cycle has five steps to ensure that businesses can leverage the technologies and efficiencies introduced by AI.
The Traditional AI/ML Cycle
The AI/ML cycle begins with defining the project objectives. Business leaders must identify a problem and subsequently find opportunities to substantially improve operations, increase customer satisfaction, or create value. Business leaders must seek out subject matter expertise to help define their unit of analysis and prediction target, prioritize their modeling criteria, consider risks and success criteria, and decide whether to continue pursuing AI/ML applications or not.
The second step in the AI/ML cycle is to acquire and explore data. Business leaders must collect and prepare all the necessary data for machine learning. This step includes conducting exploratory data analysis, finding and removing target leakage, and feature engineering.
The third step in the AI/ML cycle is to model data. Modeling data starts with determining a target variable that business leaders want to understand better. This ensures that the AI/ML application can gain insights from the initially collected data. With this, businesses will run ML algorithms to select variables, build candidate models, and validate and select an appropriate AI/ML model.
The fourth step in the AI/ML cycle is to interpret and communicate model insights. This is a challenge in machine learning projects since it entails explaining model outcomes to people who do not have a background in data science. With this, the AI/ML model must be interpretable to communicate its value to management and key stakeholders.
The AI/ML cycle ends with implementing, documenting, and maintaining the project. This includes setting up a batch or API prediction system, documenting the modeling process for reproducibility, and creating a model monitoring and maintenance plan to allow businesses to improve their AI/ML models.
Elevating the Traditional AI/ML Development Life Cycle
While the traditional AI/ML development life cycle ensures business development and value, it fails to include trust into the picture. Standards and protocols for trustworthiness must be present in the design, development, deployment, and management of your AI/ML system.
Konfer empowers businesses to elevate their AI/ML development life cycles by incorporating trust into the process. Konfer operationalizes AI trust through the Konfer Confidence Cloud, a solution that maps, measures, and manages AI-powered applications across all AI/ML development stages. The Konfer Confidence Cloud operationalizes trust by offering a rich set of capabilities:
- KonferKnowledge – the repository of all AI software assets and associated attributes, including AI/ML applications, models, feature stores, and data
- KonferConfidence – a quantitative measure that enables businesses to profile and measure AI/ML applications on performance, robustness, privacy, compliance, and regulatory risk factors
- KonferTrust – a system that generates operational alerts and reports while integrating with an organization’s service management and collaboration systems
Konfer Confidence Cloud helps businesses instill trust into their AI/ML development life cycles to achieve business continuity, improve collaboration, and increase regulatory confidence.
Set up a demo with us today to find out how you can elevate your AI/ML development life cycle.
- Research. The brief says that algorithmic systems have risks built into them and that more attention and research are needed to deal with these risks. It recommends studying human-centered software development practices, testing, audit trails, monitoring, training, and governance to identify ways to reduce risks.
- Shift focus from AI ethics to safer algorithmic systems. The lead author of the brief, Ben Shneiderman, suggests shifting the focus from AI ethics to safer algorithmic systems. He emphasizes the need to change our focus on what we do and how we make these things practical.
- Create a human-centered safety culture. The ACM brief suggests that a safety-focused culture, incorporating “human factors engineering,” must be established by organizations and integrated into the algorithmic system design to prioritize safety.
- Implement safeguards and review. Shneiderman emphasizes the need for governments and organizations to give equal importance to implementing safeguards or security measures as they do to reviewing new products or pharmaceuticals before releasing them to the public.
- Adopt safety-related practices. The brief suggests ways to enhance the safety of algorithmic systems, such as using “red team” tests and “bug bounties.” These methods have worked well to improve cybersecurity and can help reduce the risks of algorithmic systems.
- Amazon SageMaker prerequisites and features
- A step-by-step guide to creating, setting up, and configuring a Notebook instance
- How to configure SageMaker Clarify to get bias reports and transparency
- How to browse through metric namespaces to find and view metrics in SageMaker CloudWatch
- How to use SageMaker CloudTrail to enable operational and risk auditing, governance, and compliance of your AWS account
- A step-by-step guide to setting up a SageMaker Domain for SageMaker Studio users, creating a Studio Project, using SageMaker templates, and creating your own Organization templates.
- Walk-through of Model development using SageMaker Studio Projects
- KonferKnowledge is the repository of all AI software assets and associated attributes, including AI/ML applications, models, and data.
- KonferConfidence is a quantitative measure that enables businesses to profile and measure AI/ML applications on performance, robustness, privacy, compliance, and regulatory risk factors. Scores are computed using metrics and observations that are aggregated over time and available in detail as a part of the following information cards.
- KonferTrust generates operational alerts and reports; integrates with an organization’s service management and collaboration systems like Slack for the stakeholders to be alerted to noncompliance, automatically documenting the compliance status of AI/ML applications, models, and data to internal and external guidelines, standards, and directives with the help of Konfer Confidence Scores and Cards.
If consumers can demonstrate that a corporation’s AI caused them harm, a new bill will permit them to sue for damages.
How can your company prepare for these new AI regulations?
EU Is Creating New AI Regulations
To make it simpler to sue AI businesses for damages, the EU is developing new regulations. The new bill, known as the AI Liability Directive (AILD), was proposed in September 2022 and is expected to become law in a few years.
To make it simpler to sue AI businesses for damages, the EU is developing new regulations. The new bill, known as the AI Liability Directive (AILD), was proposed in September 2022 and is expected to become law in a few years.
The new legislation would authorize individuals and entities to pursue legal action for damages if an AI system caused them harm. The objective is to hold AI developers, producers, and users accountable and oblige them to disclose the development and training processes used to create their AI systems. Tech corporations that violate these rules might face class actions across the EU.
Tech Giants Complain It Will Limit Innovation
Tech companies are likely to exert significant pressure on their behalf because they believe such regulations will inhibit innovation.
The measure might have a negative effect particularly on software development, according to Mathilde Adjutor, Europe’s policy manager for the tech lobbying group CCIA, which works on behalf of businesses — including Google, Amazon, and Uber. She says that under the new regulations, developers not only run the possibility of being held accountable for software flaws, but also for the software’s potential impact on users’ mental health.
Consumer Groups Claim It Doesn’t Go Far Enough
The measure will rebalance power in favor of consumers rather than businesses, says Imogen Parker, associate director of policy at the Ada Lovelace Institute, a research center for artificial intelligence.
Some consumer rights groups and campaigners, however, think otherwise. The plans, according to them, don’t go far enough and will place an unfairly high burden on customers who wish to file claims.
The plan is a “real letdown,” according to Ursula Pachl, deputy director general of the European Consumer Organization. She says it places the burden of proof on consumers to demonstrate that an AI system injured them or that an AI developer was careless.
It will be hard for the consumer to employ the new guidelines in a world of highly cryptic and intricate “black box” AI systems, according to Pachl. She added that it would be very challenging to demonstrate that someone was the victim of racial discrimination as a result of the design of a credit scoring system.
Why should businesses care about these sentiments? Because AILD can intensify consumers’ distrust of AI and influence their adoption of the technology.
What You Can Do Now To Prepare For AILD
It will take at least two years for the AILD draft to make its way through the EU’s legislative process. For the time being, attention should be paid to improving visibility.
Only by having complete and granular visibility can your organization manage compliance and mitigate risks with confidence.
According to McKinsey, companies fail to manage AI risks because they cannot address the full spectrum of risks they are exposed to. They are also unaware of the extent of their exposure to AI risks.
The underlying issue is a lack of visibility and transparency, which is exacerbated by low-code and no-code AI solutions. Lay business people can integrate them into their processes on their own, leaving concerned units like IT and compliance in the dark.
With Konfer Confidence Cloud, you can set the visibility in place while the EU AI rules are being finalized.
How Konfer Enhances Visibility
Konfer provides a verification, visibility, and trust management framework which you can leverage to pivot and create the right guardrails once the regulations are formalized. You can then adjust the framework to capture the specifics with a complete and granular view of the organization’s AI ecosystem.
Konfer Confidence Cloud also increases AI transparency by automatically collecting all the dynamic information generated as the applications drive business results. It automatically discovers, aggregates, and builds the relationships between the assets, allowing all stakeholders to obtain all information about their AI ecosystem, including.
- which AI solutions exist across the enterprise,
- where each AI solution is located,
- who uses them,
- how users utilize them, and
- which business metrics and KPIs they influence.
Konfer offers a rich set of capabilities, including KonferKnowledge. KonferKnowledge is the repository of all AI software assets and associated attributes — including AI/ML applications, models, feature stores, and data. The KonferKnowledge graph is the single source of truth about individual AI applications.
With Konfer, it will be easier for your organization to share information on how your company develops and trains your AI systems, when needed.
Using Konfer encourages customers to trust your AI products and be confident in using them, knowing that there is a systematic, standardized, and simple process to gather evidence should AI harm them.
Furthermore, AI compliance officers can also take advantage of Konfer’s trust management system to simplify verification, documentation, and other tasks they need to accomplish to ensure compliance.
Achieve AI Confidence with Konfer
Regardless of its outcomes, the AILD will have direct and indirect effects on your business and customers. But with Konfer Confidence Cloud, you can create a verification, visibility, and trust management framework and increase regulatory confidence, knowing that your AI is transparent and trustworthy.
Do you want to see how Konfer works? Request a demo today!
How to Create a Culture of Robustness, Transparency, and Security Leading to Trusted AI/ML Solutions.
Artificial intelligence (AI) is transforming the business landscape by serving a supporting function in processing and analyzing large volumes of data. With the continuous adoption of AI technologies around the world, it is only a matter of time until it becomes an indispensable part of businesses. As such, it is unsurprising that the AI market is currently valued at USD 93.5 billion and is predicted to expand at a compound annual growth rate (CAGR) of 38.1 percent until 2030.
Despite the positive advancements introduced by AI, businesses can have reservations about adding AI technology to their systems due to ethical reasons. These issues stem from mistrust of AI, inhibiting business leaders from reaping the full benefits of this exciting new technology.
The Trust Gap
At its core, the trust gap is due to the missing foundation of similarity between humans and AI systems. Humans do not understand the motivations, reasoning methods, and reactions of AI towards certain situations. As humans, we build trust on quantifiable metrics such as track record, transparency, and responses to new situations.
In order to address the trust gap, AI must meet two levels of trustworthiness: technicality and ethicality.
Technical trust refers to trustworthiness in terms of functionality. These include accuracy, robustness, resiliency, security, and explainability, which reflects the capability of an AI system to perform its tasks with minimal error, minimal susceptibility to changes and attacks, and justifiability in actional and decisional aspects. With the level of technology currently available, most AI systems can meet these technical requirements due to the numerous quantifiable tests conducted on AI technologies to ensure repeatability, predictability, and reliability.
On the other hand, ethical trust is trustworthiness in social and ethical responsibility. This is a more pressing concern compared to technical trust since this takes on a more human approach to trust. Ethical trust includes privacy, fairness, interpretability, transparency, and accountability. These factors ensure professional competence, reputation, and good governance. However, ethical trust is hard to achieve due to bias. AI systems are highly susceptible to being infiltrated by bias since it learns how to make decisions based on the training data fed into the system. This data could potentially contain biased human decisions and historical or social inequities.
Addressing the Trust Gap
Ethicality must be built into every aspect of AI. Standards and protocols for efficacy, ethics, and trustworthiness must be present as you design, develop, deploy, and manage your AI system. As such, it is vital to operationalize AI ethics by indicating a quantifiable metric on all dimensions of AI in line with ethical standards that the system must meet.
Konfer provides a solution for businesses struggling with the ethical aspect of AI systems. Konfer operationalizes AI trust through the Konfer Confidence Cloud, a solution that helps business leaders achieve confidence in AI by elevating traditional AI/ML development life cycles into trusted AI/ML development life cycles and providing verification, visibility, and trust management framework. To do this, Konfer Confidence Cloud follows a three-step process of mapping, measuring, and managing internal and third-party AI/ML systems:
- Map — provides a global view of all your AI assets and their inter-relationships
- Measure — measures the use and outcomes of all your AI assets based on quantified parameters such as fairness, robustness, security, and performance
- Manage — drives actions by initiating reports, incidents, and cases
Konfer Confidence Cloud helps businesses avoid compliance challenges that may entail security, legal, regulatory, and financial repercussions. Konfer’s solution empowers business leaders to trust AI-powered decision-making and maintain a competitive advantage.
Set up a demo with us today to find out how your business can achieve ethical and responsible AI.
Our recent blog discussed the critical gaps businesses must address to achieve Artificial Intelligence transparency, trust, and confidence. It is crucial to address these gaps so your AI ecosystem can be trustworthy.
AI applications ingest training data to learn how to make decisions. But if training data includes biased human decisions or reflects historical or social inequities, then the AI application’s results are flawed and cannot be trusted. There have been numerous instances where AI models were unintentionally used to institutionalize bias, like the one used to predict recidivism but later turned out to be biased against black people. Another example would be the AI recruiting tool that showed discrimination against women.
To make AI trustworthy, companies need to explain the rationale behind an AI algorithm’s results and the accuracy of that algorithm in arriving at an outcome. It requires full transparency so stakeholders can understand and explain the inner workings of their AI systems. Businesses looking to deploy AI but are held back by ethical issues such as human bias should establish conscientious processes to mitigate bias. To do this, companies need to operationalize AI Trust.
Ensure Responsible and Ethical AI by Operationalizing AI Trust
How do you operationalize AI trust? At Konfer, we ensure trust in AI-powered decision-making using our Konfer Confidence Cloud.
Konfer Confidence Cloud is the end-to-end trust management system leader that can operationalize trust of AI applications for organizations delivering on their digital initiatives. It works by mapping, measuring, and managing your AI-powered applications across all stages of solution development, deployment, and production, thereby elevating trust.
Here’s how it works:
Map. You get a global view of your AI assets and their interrelationships. With a unified view and shared understanding of AI inventory, all stakeholders can collaborate more effectively to achieve confidence in their AI initiatives.
We use KonferKnowledge, a repository of all AI software assets and associated attributes, including AI/ML applications, models, feature stores, and data. The KonferKnowledge graph is the single source of truth about all the AI applications, where they exist, what resources they depend upon and share, and what metrics and KPIs they influence for the organization.
Measure. We measure the use of and outcomes of all your AI assets. Konfer empowers you with AI-powered applications with quantified parameters such as fairness, robustness, security, and performance to measure your business risks.
We use KonferConfidence, a quantitative measure that enables businesses to profile and measure AI/ML applications on performance, robustness, privacy, compliance, and regulatory risk factors. Scores are computed using metrics and observations and aggregated over time.
Manage. With a clear path to the truth, all stakeholders can drive actions by initiating reports, incidents, and cases. This way, they can manage their business metrics as outcomes of a trusted AI system.
We use KonferTrust, which generates operational alerts and reports. It works by integrating into an organization’s service management and collaboration systems like Slack for the stakeholders to be alerted to noncompliance and automatically documenting the compliance status of AI/ML applications, models, and data.
Konfer automates the mapping of your AI systems so all stakeholders can readily determine and evaluate the various facets of AI that can generate business risks and confidently manage their business metrics.
You can do more with Konfer Confidence Cloud. Do you want to see it in action? Request a demo today!
Why Confidence in Your AI Systems Is Key To Achieving Resilience In Today’s Highly Volatile Market
A recent FERMA–McKinsey survey revealed that most businesses “acknowledge that the global pandemic has made risk and resilience significantly more important to their organizations.”
McKinsey suggests, however, that companies should transition from risk management to strategic resilience to be more adaptive to disruptions — and we agree.
“The holistic approach to building resilience advances the organization from a narrow focus on risk, controls, governance, and reporting to a longer-term strategic view of the total environment,” the survey opines. “Rather than hunting for blind spots in risk coverage within today’s business model, resilient organizations embrace the holistic view, in which resilience becomes a competitive advantage in times of disruption.”
Konfer Confidence Cloud, which operationalizes AI Trust, is built on this premise. AI plays a crucial role in achieving business resilience. But society remains skeptical about using AI due to fear of potential ethical and other risk issues — and companies can address this fear more effectively using strategic resilience rather than risk management alone.
The Resilience That Stems From Confidence
AI has helped alleviate the impact of the pandemic and other business disruptions on businesses. One global company leveraged AI to monitor and determine unusual ordering patterns, which allowed them to respond accordingly. Apple and Google developed AI-powered contract tracing apps. But due to data privacy concerns, people were reluctant to use these apps.
Their fear is valid because AI systems, like any other systems, are not without flaws and vulnerabilities and may fail at any moment. And with hackers turning to AI, businesses are exposed to adversarial AI, which skews AI decisions. When AI solutions give false, unreliable, and questionable outcomes, stakeholders may become less confident in using them. It can also result in ethical issues, business disruptions, and other costly consequences.
Using a risk management approach to address these issues is no longer sufficient. Businesses must use a holistic approach to achieve AI confidence and technological resilience.
Technological resilience is not the only good outcome of AI confidence. When all stakeholders trust that their AI systems are ethical and responsible, they can confidently harness these systems to enable financial, operational, reputational, organizational, and business-model resilience.
Konfer’s Holistic Approach to Achieving AI Confidence
Konfer empowers businesses to achieve AI confidence and enable strategic resilience by using a holistic approach to operationalizing trust of their AI systems throughout their entire lifecycle, thus elevating traditional AI/ML development lifecycles into trusted AI/ML development lifecycles.
Operationalizing AI Trust is crucial in enabling AI confidence because stakeholders cannot be confident in their AI technologies if they do not trust them. And they cannot trust something that is not transparent.
Konfer Confidence Cloud provides a mechanism that empowers all stakeholders to “see” and understand the inner workings of AI systems. According to Candelon et al., “most companies still don’t build AI in such a way that they can always explain exactly how the algorithms work.” Konfer fills this gap by allowing companies to map their AI systems automatically and create a graphic visualization or a crystal clear map of their AI ecosystem. Stakeholders can use this map to determine potential ethical and risk issues which could make the businesses vulnerable.
Konfer goes beyond managing and measuring AI risks, which traditional solutions usually offer. Konfer enables a holistic approach by providing businesses with the following capabilities:
- KonferKnowledge is the repository of all AI software assets and associated attributes, including AI/ML applications, models, feature stores, and data. The Transparency delivered by the Konfer Knowledge Graph is the foundation for creating trust across AI applications.
- KonferConfidence is a quantitative measure that enables businesses to profile and measure AI/ML applications on performance, robustness, privacy, compliance, and regulatory risk factors. Scores are computed using metrics and observations aggregated over time and available in detail as a part of Application Cards, Model Cards, and Data Cards.
- KonferTrust generates operational alerts and reports. It integrates with an organization’s service management and collaboration systems like Slack to alert stakeholders of noncompliance in an automated fashion. KonferTrust automatically documents the compliance status of AI/ML applications, models, and data to internal and external guidelines, standards, and directives using Konfer Confidence Scores and Cards.
AI Confidence is key to achieving resilience in today’s digital and highly volatile market. Businesses can be confident that their AI systems are conscientiously doing their part in making the company adaptive to changes if they have full trust in these systems.
Businesses must transition from risk management to strategic resilience to achieve AI confidence and overall business resilience. Konfer enables you to do so by allowing your company to shift from a narrow focus on AI risk, controls, governance, and reporting to a longer-term strategic view of the total AI environment.
If you want to see first-hand how Konfer Confidence Cloud works, feel free to reach out and we can give you a free demo.
Artificial Intelligence (AI) applications are revolutionizing the way we do business. But the complex and abstract environment of ungoverned AI apps running across the enterprise is a ticking time bomb waiting to explode. According to a HelpNet security report, a company’s reputation and bottom line are at risk when AI interactions result in ethical issues.
In contrast, customers trust the company more when they perceive their AI interactions are ethical. The majority of surveyed customers (55%) said that “they would purchase more products [from the company] and provide high ratings and positive feedback on social media.”
This is why it is crucial that you enable AI transparency, trust, and confidence. But this is easier said than done due to the inherent complexity and abstract nature of AI systems.
You can achieve transparency, trust, and confidence in your AI by addressing the following gaps.
- Silos in your company’s AI ecosystem
Silos occur not only during AI development and production, where blended teams or various stakeholders work separately to build the product. The AI ecosystem also becomes fragmented when business units or departments develop and use disparate AI applications for different use cases. Low-code or no-code is compounding this fragmentation, making AI Auditing complicated and laborious.
Silos make it harder to govern AI assets, users, and components and monitor changes and anomalies that could lead to ethical issues and other risks. Due to silos, mapping the system to pinpoint the root cause of the problem and the possible rippling impact is challenging.
To fill this gap, you need to find a way to harmonize all players, assets, and components in your company’s AI ecosystem — which points us to the next gap.
- Lack of a global view of all AI applications, users, and asset information.
The lack of transparency and understanding of an AI system’s inner workings partly “stems from the sheer technical complexity of AI systems.” To achieve AI transparency, trust, and confidence, you need a global view of all your company’s AI applications, all AI users, and all AI components/asset information, including the following:
- Proprietary data
- Third-party data
These assets and components must be visible to all stakeholders so everyone can proactively sense and respond to any issue.
But visibility into these assets and components alone is not enough. The AI ecosystem is inherently complex, so combing through these assets and their information to pinpoint the problem will remain tedious unless a mechanism that allows for easy mapping of the AI system is in place. This leads us to the next gap.
- Lack of means to map the AI system.
An AI transparency or trust management system must fulfill three functions: Map, Measure, and Manage. But most solutions are designed for measuring and managing risks only.
Mapping is equally crucial. It helps pinpoint the what, when, where, why, and how of the problem by providing end-to-end visibility with relationships.
Mapping also helps prevent the rippling impact of the problem by allowing stakeholders to see the relationships among AI assets and components and how an issue that arose from one element can impact another or the entire AI ecosystem. It empowers them to quickly identify all components, applications, and customers that could be affected to ensure business continuity. Confidence increases when this map is crystal clear to the stakeholders.
Now, how do we fill the gaps?
To address these gaps and achieve AI transparency, trust, and confidence, take advantage of a solution that allows you to:
- Overcome silos by enabling a digital holistic overview of all your company’s AI assets, components, and users.
- Enable a robust AI transparency/trust management system that allows for Mapping, Measuring, and Managing the entire AI system to prevent/deal with issues and risks in real-time.
- Create an operational framework of robustness, transparency, and security, leading to Trusted AI/ML solutions.
- Elevate Traditional AI/ML Development Life Cycles into Trusted AI/ML Development Life Cycles
This is where Konfer Confidence Cloud comes in.
Konfer Confidence Cloud service empowers your organization to be “confAIdent” about your AI initiatives by providing a unified view of your fragmented AI ecosystem and a holistic governance and risk management framework to safeguard you across the AI lifecycle.
Konfer automates the mapping of your AI systems so all stakeholders can easily determine and evaluate the various facets of AI that can generate business risks and confidently manage their business metrics.
By operationalizing Trust of your AI framework, you can avoid unintended consequences and compliance challenges that are not only harmful to businesses, but also could result in security, legal, regulatory, and financial troubles.
Konfer Confidence Cloud increases the trust of AI-powered decision-making, allowing your organization to reap various competitive advantages including business continuity, improved collaboration, and increased regulatory confidence.
Drop us a line if you want to get first-hand experience of how Konfer’s AI Transparency Cloud helps achieve confidence in your AI.