Ethical AI implementation: Fabrity's human-centric approach to progress

Ethical AI implementation refers to the development and deployment of artificial intelligence (AI) systems in a manner that aligns with ethical principles and societal values. It involves ensuring that AI technologies are designed, built, and used in ways that respect human rights, fairness, transparency, and accountability.

At Fabrity, we firmly believe that AI should be a tool that empowers and uplifts humanity, rather than a force that replaces or diminishes it. Our framework for ethical AI implementation is grounded in principles that prioritize human well-being, transparency, and accountability.

Key aspects of ethical AI implementation

1.????? AI systems should be designed to be transparent and explainable, meaning their decision-making processes and outputs can be understood and audited by humans. This helps to ensure that AI is used responsibly and fairly, and that any potential biases or errors can be identified and corrected.

2.????? AI systems should be designed to avoid bias and discrimination based on factors such as race, gender, age, or socioeconomic status. This requires careful consideration of the data used to train AI models, as well as the potential impact of AI on different groups of people.

3.????? AI systems should be designed with privacy and security in mind, ensuring that personal data is collected, stored, and used in a responsible and secure manner. This includes protecting data from unauthorized access, ensuring data accuracy and integrity, and providing individuals with control over their data.

4.????? AI systems should be designed with clear lines of accountability and responsibility, ensuring that those who develop, deploy, and use AI are held accountable for its impacts. This includes having mechanisms in place to address and rectify any harm or unintended consequences caused by AI.

5.????? AI systems should be designed with human oversight and control, ensuring that humans remain in charge of critical decision-making and can intervene if necessary. This helps to prevent AI from becoming autonomous and potentially causing harm.

Against the background of general guidelines, the ISO standard ISO/IEC TR 24368:2022 comes in handy, and its guidelines cover the following areas:

1.????? Accountability: Establish mechanisms for oversight and responsibility in AI decision-making, ensuring avenues for redress.

2.????? Fairness and non-discrimination: Design AI systems to promote equality and avoid biases, ensuring fair treatment for all.

3.????? Transparency and explainability: Ensure AI operations are clear and understandable, fostering trust through explainable decisions.

4.????? Professional responsibility: Adhere to ethical guidelines and professional standards in AI development and deployment.

5.????? Promotion of human values: Align AI with societal and cultural values to enhance human well-being and support societal goals.

6.????? Privacy: Protect personal data and privacy rights, ensuring responsible data handling practices.

7.????? Safety and security: Ensure AI systems are safe, secure, and resilient against attacks, protecting users from harm.

8.????? Human control of technology: Maintain human oversight and control over AI systems, allowing intervention or override when necessary.

9.????? Community involvement and development: Engage communities in the AI development process, addressing local needs and promoting inclusive growth.

10.? Human-centered design: Focus on usability and accessibility in AI design, ensuring systems are user-friendly for diverse populations.

11.? Respect for the rule of law: Comply with laws and regulations, supporting legal frameworks and avoiding illegal activities.

12.? Respect for international norms of behaviour: Align AI practices with international ethical standards, fostering global cooperation and preventing harm.

13.? Environmental sustainability: Consider environmental impacts in AI development, promoting sustainability and reducing ecological footprints.

14.? Labour practices: Support fair labour practices, avoid harm to workers, and contribute positively to employment conditions.

The human element: augmenting, not replacing

The ongoing discussion surrounding the integration of artificial intelligence (AI) in the workplace has raised valid concerns about potential job displacement. While the fear of automation is understandable, a more nuanced approach is emerging that emphasizes the potential for AI to augment human capabilities rather than replace them. This human-centric philosophy posits that AI should be leveraged to take over repetitive, mundane tasks, freeing up human workers to focus on higher-order activities that require creativity, strategic thinking, and complex problem-solving. By automating routine processes, AI can streamline workflows, increase efficiency, and ultimately create a more fulfilling and engaging work experience for individuals. This approach not only addresses the concerns of job displacement but also highlights the potential for AI to enhance human productivity and innovation. By taking on tasks that are better suited for machines, humans are liberated to focus on their unique strengths, leading to greater job satisfaction and a more valuable contribution to the workforce. Furthermore, this approach recognizes that certain skills, such as creativity, critical thinking, and emotional intelligence, are inherently human and cannot be easily replicated by machines. By focusing on these areas, individuals can develop their unique talents and remain valuable contributors in an increasingly AI-powered workplace.

However, a key aspect of this integration is to seek out processes that not only help people achieve more in terms of their knowledge and abilities but also identify those recurring tasks that cannot be efficiently addressed by Robotic Process Automation (RPA) or Enterprise Resource Planning (ERP) workflows. In such scenarios, AI can function as a personal assistant or concierge, providing tailored support to individual workers. This approach presents a significant opportunity to enhance human abilities and focus on activities that expand creativity and effectiveness. By embedding AI into roles that act as personal assistants, employees can receive customized support that enhances their capabilities. AI can assist with tasks such as scheduling, information retrieval, and data analysis, allowing workers to dedicate more time to innovative and strategic initiatives. This integration not only optimizes existing workflows but also empowers employees to push the boundaries of what they can achieve, fostering a more dynamic and productive work environment.

Ultimately, the true value of AI in the workplace lies in its ability to complement and extend human abilities. By automating repetitive tasks and providing intelligent support, AI can help workers focus on higher-order activities that drive personal and professional growth. This symbiotic relationship between humans and AI can lead to a more creative, effective, and satisfying work experience, ensuring that individuals continue to be essential and valuable contributors in the evolving landscape of work.

Transparency and explainability

Fabrity addresses this challenge by prioritizing transparency and explainability in AI development. We strive to create AI models that are interpretable, meaning their decision-making processes are clear and understandable to humans. This is achieved through a variety of techniques, such as incorporating explainable features into the model's architecture, providing detailed documentation of the model's training data and algorithms, and conducting thorough testing and validation to ensure accuracy and reliability.

By making AI systems more transparent, we can better understand how they arrive at their conclusions and identify any potential biases or errors. This transparency fosters accountability, as it allows for independent audits and assessments of the AI's decision-making process. Additionally, it enables continuous improvement by identifying areas where the model can be refined or optimized.

Data ethics and privacy

Fabrity recognizes the critical importance of upholding stringent data ethics and privacy standards in AI development. We prioritize obtaining informed consent before collecting any data, ensuring that individuals understand how their information will be used and have the autonomy to make informed decisions. Furthermore, we are committed to anonymizing data whenever possible to protect individual privacy. By removing personally identifiable information, we minimize the risk of data breaches and unauthorized access. Additionally, we implement robust security measures to safeguard sensitive data, employing state-of-the-art encryption and access controls to ensure confidentiality and integrity.

Fabrity firmly believes that individuals should have control over their data and be aware of how it is used. We strive for transparency in our data practices, providing clear and accessible information about our data collection, storage, and usage policies. By empowering individuals with knowledge and control, we foster trust and respect for their privacy rights.

Addressing bias and fairness

Fabrity recognizes the importance of addressing bias and ensuring fairness in AI outcomes. We actively work to identify and mitigate biases throughout the entire AI development lifecycle. This includes carefully curating diverse datasets that accurately represent the populations our AI systems will interact with, minimizing the risk of skewed or discriminatory results. We also employ rigorous testing methodologies to evaluate AI models for potential biases and unintended consequences. By simulating various scenarios and analyzing model outputs, we can identify and address any discriminatory tendencies before deployment. Furthermore, we recognize that addressing bias is an ongoing process. We engage in continuous monitoring of our AI systems, analyzing their performance in real-world situations to detect and correct any emerging biases.

Human-in-the-loop

This human-centric approach recognizes the unique value of human judgment, intuition, and ethical considerations, which cannot be easily replicated by machines. While AI systems can provide valuable insights and recommendations, they should not be seen as a replacement for human decision-makers. Instead, AI should serve as a tool to enhance human capabilities, providing additional information and analysis to inform decision-making.

By keeping humans in the loop, Fabrity ensures that AI systems are used responsibly and ethically.

Human-in-the-loop is a human-centric approach that integrates human judgment, intuition, and ethical considerations into the operation of AI systems. This approach acknowledges that certain aspects of decision-making—such as ethical discernment and contextual understanding—are uniquely human and cannot be easily replicated by machines.

This approach allows for human oversight and intervention, especially in situations where AI may lack the nuanced understanding or contextual awareness necessary to make sound decisions. Furthermore, the human-in-the-loop approach fosters a sense of accountability and responsibility. By maintaining human involvement in decision-making, we ensure that individuals are ultimately responsible for the outcomes of AI systems and can be held accountable for any unintended consequences. This approach promotes transparency and trust in AI technology, as it demonstrates a commitment to ethical considerations and human values.

Continuous learning and improvement

We understand that ethical considerations in AI are not static but evolve alongside the technology itself. Therefore, we actively engage in continuous learning and improvement to ensure that our AI systems remain aligned with our core values and the changing needs of society. This commitment involves actively participating in the broader AI ethics community, engaging in discussions and collaborations with experts, researchers, and stakeholders. We continuously monitor and evaluate the impact of our AI systems, gathering feedback and insights to identify areas for improvement and address any potential ethical concerns.

By staying abreast of the latest research, regulatory developments, and societal discussions, we can adapt our AI systems to meet emerging ethical challenges and ensure that they are used responsibly and for the benefit of society. Our dedication to continuous learning and improvement reflects our belief that ethical AI is an ongoing process that requires constant vigilance and adaptation to ensure that AI technology is aligned with human values and serves the greater good.

Conclusion

At Fabrity, we are proud to be pioneers in ethical AI implementation. Our human-centric approach, encapsulated in our BPIS framework (Business Process Identification Session), stands on strong foundations of ISO/IEC TR 24368:202, prioritizes the well-being and empowerment of individuals while harnessing the transformative power of AI. By embracing transparency, accountability, and fairness, we strive to create AI solutions that contribute to a better future for all.

Our BPIS framework ensures that every step of our AI development and deployment process is guided by ethical principles. We actively mitigate biases in data and algorithms, protect user privacy through robust security measures and anonymization, ensure that AI decision-making processes are transparent and interpretable, and maintain the highest standards of security to safeguard sensitive information. We focus in this process mainly on Personas and their needs. Then we seek the processes under the business process management. We are a mediator among business interest of operations, IT, sales, marketing and C-level. But we do not forget about the end-user personas, which may be both, internal and external. This the art, we’ve done since 20 years, in more than 300 projects enterprise class and hundreds of small type projects.

By adhering to these ethical principles and continuously learning and adapting to the AI landscape, we are confident that our AI solutions will not only be innovative and effective but also responsible and beneficial to society as a whole. We believe that by placing ethical considerations at the forefront of AI development, we can build a future where AI truly serves humanity, fostering a more equitable, inclusive, and prosperous world.

Meet with me to discuss ethical AI implementation in your organization.

Greg Spercz

Digital Transformation Manager

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Cases we did:

1. Industrial

• Advanced documentation processing: Supporting production and service processes involving complex documentation sets.
• Production monitoring & improvement: Connecting AI with industrial applications for enhanced monitoring and process optimization.
• Process automation: Automating problematic production processes to boost efficiency and profitability without indiscriminate automation.
• Field support: Assisting service technicians with AI to query documentation in real-time.

2. Marketing

• Documentation processing: Recognition of new products and value propositions, pattern detection.
• E-commerce automation: SKU processing, content generation, process automation, and support for new sales channels in B2B.
• Customer service enhancement: Relieving customer service through AI, sales profiling assistance, sentiment analysis, and automatic ticketing.

3. Pharma & bio-tech

• Documentation processing: Supporting production and service processes, predicting new medicine combinations, and data mining for Medical R&D.
• Process automation: Using AI to predict sales increases correlated with pathogen distribution and pharmacy data.

4. Healthcare

• AI in pre-triage and medical qualification: Utilizing case databases to accelerate serious case analysis and appropriate service referrals.
• Customer service optimization: Building patient portals and managing medical records with AI support.
• Disease prediction: AI support for doctors in predicting possible diseases.

5. Banking

• AI-Powered fee and commission analysis: Multi-threaded analysis of fee tables, document integration, and automated alerting for price list changes.
• Automated reporting: Generating reports and automatic ticketing in CRM systems via API connections and voice interfaces.

General Approach

• Combining AI with Low Code: Across all sectors, the integration of AI with low code (especially our Home - Fastive.com with use of frameworks enhancing customization and efficiency.
• Knowledge Sharing and PoC Development: Encouraging exchanges of ideas and developing Proof of Concepts (PoCs) through meetings with CTOs and industry analysts.