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It aims to foster 15 responsible, trustworthy AI systems for critical domains such 16 as healthcare. By developing human-centered methods and 17 techniques to integrate fairness, privacy, adaptability, and 18 explainability in AI systems, FATE seeks to ensure the 19 reliability and ethical use of AI in complex domains.`,img:{url:"fate-logo-white",height:120,type:"svg"}},dataset:[{id:"project-container",title:"Relevance to Type 2 Diabetes",description:`The leading use case for FATE (2023-2026) is decision support in preventive healthcare (in particular type 2 diabetes (T2D). AI-based decision support provides compelling improvements and challenges for preventive healthcare, such as: <ol class='ml-5 mt-2'> 20<li> 21predicting the behaviour of patient populations, which 22allows care providers to act before an illness occurs; 23</li> 24<li> 25calculating individual risk based on demographics, 26comorbidities, medications, labs, imaging, and social 27determinants of health; 28</li> 29<li> 30identifying patients with higher apparent risk factors for 31certain chronic or serious conditions, allowing healthcare 32providers to prioritize screening and prevention efforts for 33those patients; 34</li> 35<li>
36enabling more effective and efficient disease diagnostics 37based on historical data, and 38</li> 39<li> 40reducing waiting times for patients and improving efficiency 41in hospitals and health systems. 42</li></ol><p class='mt-2'>The technology developed can be applied in many other domainsthat require decision support.</p><p class='mt-2'>More detailed information on each FATE topic can be found onthe corresponding information pages on Fairness,Confidentiality, and Adaptivity.</p>`,img:{url:"sugar-blood-level_3209082",height:120,type:"png"},color:"grey lighten-4"}]}}});function Vv(e,t,n,s,i,r){const o=ht("page-topic-section"),a=ht("result-section"),l=ht("contact-us");return Te(),dt(_e,null,[v(o,{title:e.wpTitle.title,id:e.wpTitle.id,description:e.wpTitle.description,img:e.wpTitle.img},null,8,["title","id","description","img"]),v(a,{data:e.dataset},null,8,["data"]),v(l)],64)}const Nv=At(Mv,[["render",Vv],["__scopeId","data-v-55029150"]]),Dv={__name:"About",setup(e){return(t,n)=>(Te(),Nt(Nv))}},jv=on({name:"WorkPackage4",components:{ResultSection:Ai,PageTopicSection:Cs},data(){return{wpTitle:{id:"wp4-header",title:"Adaptivity and Interaction",description:"Combining human and machine intelligence for better decision-making is an important challenge in the development of responsible AI. As information and knowledge is continuously evolving, AI systems should be able to learn and improve over time based on new data and insights. Our methods combine data-driven and knowledge-driven techniques that allow an AI system to evolve over time by integrating new information (e.g., from literature, user data, feedback) in its models.",image:{url:"adaptivity-logo",height:120,type:"svg"}},dataset:[{id:"wp4-container",title:"Relevance to Type 2 Diabetes",description:"With the growing pressure on healthcare professionals, there is a need for initiatives that empower patients to take control of their lifestyle. We investigate an AI system that can give advice to people at risk for type 2 diabetes (T2D). Such a system needs to be adaptive to new information: new medical insights and patient information should be included to give accurate and relevant advice.",img:{url:"sugar-blood-level_3209082",height:120,type:"png"},color:"grey lighten-4"},{id:"wp4-highlights",title:"Highlights",description:`In 2023, we created the system architecture shown on the left and did experiments on: 43<ul><li>Updating a Knowledge Graph with new medical publications</li><li>Evaluating the changes in the graph</li><li>A Conversational Agent that can gather information from the patient</li><br>In 2024, we will research: 1. Graph-based feature engineering and 2. Adaptive fact integration from user and data in the system. `,img:{url:"adaptivity-system",height:500,type:"svg"},color:"grey lighten-4"},{id:"wp4-techniques",title:"Techniques",description:`We focus on a set of techniques. These include: 44<ul><li>Knowledge Graphs for Dynamic Data</li><li>Factual LLMs</li><li>Evaluation Metrics for Graphs</li><li>Conversational Agents for the Health Domain</li>`,img:{url:"techniques",height:120,type:"png"},color:"grey lighten-4"},{id:"wp4-impact",title:"Impact",description:"The development of an adaptive system for T2D management allows for interaction and integration of new medical insights, empowering patients with personalized and up-to-date healthcare advice. The innovative integration of knowledge graphs and prediction models facilitates seamless incorporation of evolving medical knowledge into tailored recommendations. Through our research, we aim to demonstrate the effectiveness of these techniques, showcasing their potential for advanced recommender systems in healthcare.",img:{url:"impact",height:120,type:"png"},color:"grey lighten-4"}]}}});function Hv(e,t,n,s,i,r){const o=ht("page-topic-section"),a=ht("result-section");return Te(),dt(_e,null,[v(o,{title:e.wpTitle.title,id:e.wpTitle.id,description:e.wpTitle.description,img:e.wpTitle.image},null,8,["title","id","description","img"]),v(a,{data:e.dataset},null,8,["data"])],64)}const Wv=At(jv,[["render",Hv]]),zv=on({name:"WorkPackage2",components:{ResultSection:Ai,PageTopicSection:Cs},data(){return{wpTitle:{id:"wp2-header",title:"Fairness and Explainability",description:"Fairness is one of the core principles for responsible AI. AI systems should avoid discriminatory impacts and unfair biases, and promote equitable access and use. In FATE, we develop methods to i
44dentify and mitigate bias in the data and explanations of AI systems, in order to promote a fair decision-making process for each subgroup of a population. For example, our methods detect imbalances in the quality of system outputs for different patient subgroups.",image:{url:"fairness-logo",height:120,type:"svg"}},dataset:[{id:"wp2-container",title:"Relevance to Type 2 Diabetes",description:"Type 2 Diabetes (T2D) is a lifestyle disease which typically requires a lot of involvement and personal responsibility from a patient. With the growing pressure on healthcare professionals, there is a need for initiatives that empower patients to take control of their lifestyle. We investigate an AI system that can give advice to people at risk for T2D. Such a system needs to be fair and explainable: different people should get equally good advice with equally good explanations.",img:{url:"sugar-blood-level_3209082",height:120,type:"png"},color:"grey lighten-4"},{id:"wp2-highlights",title:"Highlights",description:"In 2023, we created modules to:<ul><li>Predict whether someone will develop T2D</li><li>Give counterfactual explanations (CFs)</li><li>Measure the quality of CFs per sensitive subgroup</li><br>In 2024, we will execute a user study to investigate the impact of unfair explanations on decision-making. For example, if female patients receive a lower quality explanation than male patients, is the consecutive decision made also of lower quality?",img:{url:"highlights",height:120,type:"png"},color:"grey lighten-4"},{id:"wp2-techniques",title:"Techniques",description:`We focus on a set of techniques. These include: 45<ul><li>Prediction models to predict if someone will develop type 2 diabetes</li><li>Counterfactual explanations to explain predictions from the prediction model</li><li>Quality metrics for counterfactual explanations</li><li>Fairness metrics to measure the fairness of explanations </li><li>Fairness metrics to measure the fairness of predictions</li>`,img:{url:"techniques",height:120,type:"png"},color:"grey lighten-4"},{id:"wp2-impact",title:"Impact",description:"We investigate fairness of explanations to ensure healthcare solutions of the future provide everyone with fair explanations. We are already able to measure the fairness of explanations, and we are currently gaining insight into how the fairness of explanations impacts decision-making.",img:{url:"impact",height:120,type:"png"},color:"grey lighten-4"}]}}});function Uv(e,t,n,s,i,r){const o=ht("page-topic-section"),a=ht("result-section");return Te(),dt(_e,null,[v(o,{title:e.wpTitle.title,id:e.wpTitle.id,description:e.wpTitle.description,img:e.wpTitle.image},null,8,["title","id","description","img"]),v(a,{data:e.dataset},null,8,["data"])],64)}const Kv=At(zv,[["render",Uv]]),qv=on({name:"WorkPackage3",components:{ResultSection:Ai,PageTopicSection:Cs},data(){return{wpTitle:{id:"wp3-header",title:"Privacy and explainability",description:"Two additional pillars of responsible AI are explainability and privacy. Explainable AI techniques allow humans to understand why a machine learning model makes certain decisions. This is particularly important in high-stake domains, such as healthcare. With privacy-preserving techniques, we ensure optimal use of confidential data from various sources, by enabling secure data sharing without harming the privacy of individuals.",image:{url:"confi-logo",height:120,type:"svg"}},dataset:[{id:"wp3-container",title:"Relevance to Type 2 Diabetes",description:"<p>Privacy of patient data is essential for responsibly adopting AI in practice. In FATE, we investigate how to remediate privacy issues in order to be able to use and learn from the sensitive data without having to share it with others. Figure 1 shows an example interaction between the FATE system, general practitioner (GP) and a patient. A GP can query the FATE system and ask for a prediction of whether a patient is at risk of developing T2D.</p>",img:{url:"privacy-system",height:400,type:"svg",caption:"Figure 1: Example interaction with FATE system."},color:"grey lighten-4"},{id:"wp3-highlights",title:"Highlights",description:"<ul><li>Generating privacy-preserving explanations based on confidential data.</li><li>Identifying and mitigating privacy risks in explainable AI technologies</li><li>Recognizing trade-offs between privacy and explainability </li>",img:{url:"highlights",height:120,type:"png"},color:"grey lighten-4"},{id:"wp3-techniques",title:"Techniques",description:`We focus on a
45set of techniques. These include: 46<ul><li>Counterfactual explanations </li><li>K-anonimity methods </li></ul><p class='mt-1'>We apply a technique called counterfactual explanations to enable the system to provide a meaningful explanation of its prediction. With this technique, another (highly similar) datapoint is searched such that the differences from the patient are minimal but just enough to not be at risk of developing diabetes. For example, the counterfactual datapoint (patient) could have the same age, job and diet as the referenced patient but exercises more often. An example of such an explanation can be found in Figure 2. Since these explanations use other persons who are very similar to the patient as evidence, there is a high risk of recognizing someone in the explanation and therefore learning additional, sensitive information about that person. We have designed a method to minimize these privacy risks by applying k-anonimity techniques to the explanation such that an individual is guaranteed to not be recognizable from the explanation because at least some number k of persons are encompassed by the explanation. Instead of giving the details of an actual person as the explanation, we for example explain âa person between 50 and 60 years oldâ instead of giving their actual age. </p>`,img:{url:"graph",height:400,type:"svg",caption:"Figure 2: Anonymous counterfactual. We first find the private counterfactual and compute ranges of Cholesterol and BMI such that enough persons are encompassed."},color:"grey lighten-4"},{id:"wp3-impact",title:"Impact",description:"We can guarantee the privacy of all people involved in the dataset, and optimally utilize the combined power of confidential datasets through secure data sharing between parties. 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