• 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

In its new brief, the global Technology Policy Council of the Association for Computing Machinery (ACM) emphasizes the need to reduce the risks associated with algorithmic systems. The council warns about these risks not being addressed despite widespread usage. What practical steps can organizations take to reduce risks associated with algorithmic systems? The council provides practical steps organizations can take to reduce the risks and make algorithmic systems safer.

• 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.

Shneiderman says that these efforts can give businesses a competitive edge. He says that investing in safety and a culture of safety is not only a moral obligation but also a wise business decision. Why embrace a holistic approach to risk management? Embracing a holistic approach is a wiser business decision. By following a holistic approach, organizations can achieve greater resilience and survive in a highly volatile market. This approach, as McKinsey suggests, shifts the focus from a narrow view of risk and controls to a broader, strategic view of the entire environment. According to McKinsey, resilient organizations embrace the holistic view rather than hunt for blind spots in risk coverage within today’s business model. The focus is not only on ethics, risks, or safety. Resilience is also important. Like cybersecurity, AI is constantly evolving and changing. Businesses need to do more than just “react to safety risks.” They need an encompassing approach to achieve technological, operational, financial, organizational, reputational, and business-model resilience. The crux of the matter is AI Transparency and Trust Shneiderman compares the creation of safer algorithmic systems to the development of civil aviation. Civil aviation is considered safe despite its inherent risks. People consider something safe if they trust it. And people will only trust something if it is transparent. They need proof to believe and be confident that it is safe indeed. Unlike civil aviation, however, AI is a black box. We cannot see its “wiring” with the naked eye. We cannot simply open the hood, identify what is wrong, and fix it. With a solution that operationalizes AI Trust, companies can achieve confidence in AI. This is where Konfer comes in. Konfer’s holistic solution operationalizes AI Trust Konfer, the end-to-end AI Trust Management System leader, enables resilience that stems from trust and confidence. Konfer empowers organizations to build a culture of robustness, transparency, and security. It uses a holistic approach to operationalizing trust of AI systems throughout their end-to-end lifecycle. Konfer provides a mechanism that empowers all stakeholders to map their AI systems automatically and visualize what is “under the hood” into a crystal-clear map of their AI ecosystem. Stakeholders can use this map to pinpoint potential issues and proactively manage their business metrics to achieve their desired results. Konfer goes beyond monitoring and measuring AI risks. It provides an all-encompassing strategy by giving companies powerful capabilities, including KonferKnowledge, KonferConfidence, and KonferTrust. Conclusion It is mission-critical to reduce the risks associated with algorithmic systems. But, as McKinsey suggests, the key to becoming more resilient is to take a holistic approach. Konfer offers a holistic solution that operationalizes AI Trust. Konfer provides a mechanism for stakeholders to understand the inner workings of AI systems. It offers advanced capabilities to map AI systems, identifies potential dangers and problems, and addresses challenges beyond conventional solutions. If you want to learn more about Konfer’s holistic solution, please reach out to us at konfer.ai.Building Trusted and Transparent AI/ML Solutions on Amazon SageMaker Data is the backbone of modern business, and this is why companies have put their trust in data-driven decision-making with the help of AI. But in the landscape of AI-supported enterprise decision-making, accuracy and consistency issues threaten this trust. The need for transparency and trust in AI A behavioral science study found that improving perceived transparency in AI decision-making increased effectiveness, which in turn encouraged trust. In the meantime, lack of openness increased discomfort, which in turn prevented trust. If ignored, users’ lack of trust in the algorithm might easily develop into a serious issue. The accuracy of the automated analysis will be contested by stakeholders. Companies must concentrate more than ever on creating AI Trust and ensuring that AI is reliable. This can be done by making the AI transparent so that interested parties can comprehend how the system operates. Building trusted and transparent AI solutions using Amazon SageMaker Amazon SageMaker is a fully managed machine learning service that allows data scientists and developers to quickly and easily build and train machine learning models, then directly deploy them into a production-ready hosted environment. It enables them to build, train, and deploy machine learning (ML) models for any use case with fully managed infrastructure, tools, and workflows. SageMaker enables more people to innovate with ML through a choice of tools — such as IDEs for data scientists and no-code interface for business analysts. With SageMaker, data scientists and developers can: access, label, and process large amounts of structured data (tabular data) and unstructured data (photo, video, geospatial, and audio) for ML; reduce training time from hours to minutes with optimized infrastructure; boost team productivity up to 10 times with purpose-built tools; and automate and standardize MLOps practices and governance across your organization to support transparency and auditability. Konfer (konfer.ai) has recently published our latest e-book designed to help organizations experience the full benefits of AI by ensuring transparency, trust, and truth in the AI/ML solutions they build. The e-book covers:

• 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

The e-book is a comprehensive guide to using SageMaker to build not just AI/ML solutions — but trusted and transparent AI/ML solutions. To download a copy of the e-book, simply go to this download link. Increase AI Trust with Konfer Konfer prioritizes transparency in all AI/ML development stages by automatically mapping, measuring, and managing your AI framework. Our flagship solution, Konfer Confidence Cloud, helps businesses improve collaboration to achieve maximum productivity and efficiency. This helps businesses improve AI traceability and understanding, thereby empowering business leaders to trust AI-powered decision-making and maintain a competitive advantage. Contact us today to find out how your business can help you achieve AI Transparency, Trust, and Confidence in your AI development and production.“Operationalizing AI Trust,” in simple terms, means creating a quantifiable measurement of all of the AI’s dimensions. But how can companies create that quantifiable measurement and which dimensions should they measure? A recent survey by IBM reveals that despite the need to advance ethical AI, there’s still a gap between business leaders’ intentions and meaningful actions as well as between humans and AI. While 80% of CEOs are ready to embed AI ethics into their business practices, less than 25% have operationalized them. Moreover, less than 20% said their company’s actions were consistent with its AI ethics principles. The disconnect between humans and AI stems from the absence of the foundation of similarity. Humans do not understand the motivations, reasoning methods, and reactions of AI towards certain situations. To achieve ethical AI, companies need to operationalize AI Trust. It’s a non-negotiable requirement. Clients, partners, and all other stakeholders can never feel confident that the AI system is responsible and ethical unless the AI is transparent. That is, making AI solutions explainable so that stakeholders can understand the inner workings of their AI systems or why their AI solutions do what it has been programmed to do. It is imperative that companies must build ethicality into every aspect of AI. Standards and protocols for efficacy, ethics, and trustworthiness must be present in the design, development, deployment, and management of the AI system. As such, it is vital to operationalize AI ethics by indicating quantifiable metrics on all dimensions of AI in line with ethical standards that the system must meet. These quantifiable metrics must include track records, transparency, and responses to new situations. In this regard, AI must meet two levels of trustworthiness: technical trust and ethical trust. Technical trust refers to trustworthiness in terms of functionality, including accuracy, robustness, resiliency, security, and explainability. This covers the capability of an AI system to perform its tasks with minimal error, less susceptibility to modifications, and justifiability in actional and decisional aspects. Ethical trust is trustworthiness in social and ethical responsibility. This covers privacy, fairness, interpretability, transparency, and accountability. Ethical trust, however, is susceptible to human bias that may have been fed into the system during the development of the AI model. This is where solutions such as Konfer Confidence Cloud can operationalize trust in AI applications for organizations. While ethical trust is hard to achieve — because AI systems are susceptible to being infiltrated by bias from the training data fed into the system — Konfer Confidence Cloud is designed to perform “mapping,” which automatically gathers all data used for AI modeling, development, etc. It then measures and manages AI-powered applications across all stages of solution development, deployment, and production, thereby elevating trust. By achieving AI Transparency and operationalizing AI Trust, organizations can obtain sufficient evidence to assume that their AI system is responsible and ethical. And when the evidence says that their AI is responsible and ethical, they can be confident in their AI solutions. Operationalize AI Trust with Konfer Konfer provides a solution for businesses struggling with the ethical aspect of AI systems. To operationalize the principles of trust, Konfer Confidence Cloud offers a rich set of capabilities:

• 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.

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.

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.

Conclusion

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.

1. 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.

2. 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:

• Models
• Logic/Provenance
• 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.

3. 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.

Conclusion

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.