You are a helpful assistant in evaluating the quality of the outputs for a given instruction. Your goal is to select the best output for the given instruction.

Select the Output (a) or Output (b) that is better for the given instruction. The two outputs are generated by two different AI chatbots respectively.

Here are some rules of the evaluation:
(1) You should prioritize evaluating whether the output honestly/precisely/closely executes the instruction, then consider its helpfulness, accuracy, level of detail, harmlessness, etc.
(2) Outputs should NOT contain more/less than what the instruction asks for, as such outputs do NOT precisely execute the instruction.
(3) You should avoid any potential bias and your judgment should be as objective as possible. For example, the order in which the outputs were presented should NOT affect your judgment, as Output (a) and Output (b) are **equally likely** to be the better.

Do NOT provide any explanation for your choice.
Do NOT say both / neither are good.
You should answer using ONLY "Output (a)" or "Output (b)". Do NOT output any other words.

# Instruction:
Here is an article:
These teenagers all have ADHD and have been excluded from mainstream school. Here they tell how the sport of boxing is helping them channel their excess energy and improve their life skills. By Gwyneth ReesBBC News In an amateur boxing gym in Barry, 14-year-old Levi is tugging on his boxing gloves, ready to join his friends in training. Some are already practising in the ring, aiming jabs and uppercuts. Others are punching the bags or skipping as music plays in the background and the smell of sweat and chalk permeates the air. Levi is a fighter. He always has been. In fact, he has been excluded from school twice for disobedient behaviour. But now he's using the discipline of boxing to channel his fighting spirit in a more positive way. Every Thursday, he partakes in a two-hour non-contact boxing session at Colcot Amateur Boxing Club, designed to help pupils who have been excluded from mainstream school. "I used to lose my temper easily," Levi explains. "I started a lot of fights and used to mess up the classrooms. "I used to be really disrespectful to the teachers and felt so down all the time, thinking I was just a naughty kid." Once excluded, however, and sent to a pupil referral unit in the Vale of Glamorgan, Levi got diagnosed with attention deficit hyperactivity disorder (ADHD) - a condition which causes inattentiveness, impulsiveness and an inability to stick to tasks. It is also closely linked with underachievement at school, poor social interaction and problems with discipline. "The diagnosis and medication helped," he explains. "But it's the boxing that really helps as I'm always so full of energy and struggle to concentrate. "I get excited and want to train my best. "Anthony Joshua is my role model and I want to follow in his steps. "But my main aim now is just to get my head down and get my GCSEs." It is a similar story for Aalijah, 14. She too has ADHD and her inability to concentrate meant she fell behind academically from a young age, causing her behaviour to spiral. Frustrated with school and feeling like she constantly had to "catch up", she was eventually excluded for intimidating her teachers. "I don't like school," she explains. "It puts me under pressure and frustrates me. "If I try to concentrate on something, my mind is just blank… and with the ADHD, I can't control what I do. It's like my body is locked back and in its own trance." But if Aalijah struggled with school, in boxing she has found something she loves. "The boxing really helps," she explains. "It lowers my energy and calms me down, helping me to concentrate. "After it, I feel a lot calmer and more sensible." Aalijah and Levi are just two of the teenagers helped by the weekly boxing sessions. Run by Empire Fighting Chance, a Bristol-based charity that launched in south Wales in 2016, the classes are designed to teach confidence and resilience, improving the life chances of those involved. It is now expanding across the whole of Wales, with sessions due to begin in Rhyl, north Wales, and other locations by the end of the year. Explaining the benefits, coach Nathan Powell, says: "Many of the kids we see have anxiety and confidence issues and struggle to work with others. "At the start of the sessions, team activities always end in arguments and confrontation. "But with work and drills, their confidence grows and they become a member of a team. These kids are also used to failing, so we break things down into mini goals, like building up the number of skips they can do. "This helps them reach a target and achieve something. It's very important and makes them feel good about themselves." A third pupil, Jamie, 11, got excluded from primary school when he was just nine despite teachers knowing about his diagnosis. "Anger is a big part of my ADHD," he says. "If I get annoyed it takes me a long time to calm down. "But the boxing helps me get my anger out and it helps me control it." As for Jamie Parry, head of business development for Empire Fighting Chance, he believes that mainstream education might not be for everyone. "Many children need a different way of learning," he says. "We try to boost aspiration by providing good role models in the form of our coaches, and we drop in personal development tips about nutrition and sleep. "The pupils often see teachers as a negative sign of authority so we try to build trust with them." According to statistics from the UK ADHD Partnership, children with ADHD have more than 100 times greater risk of being permanently excluded from school than other children. Roughly 40% of children with ADHD have had fixed-term exclusions from school and 11% have been permanently excluded. Susan Young, president of the UK ADHD Partnership, said: "Sporting activities, such as boxing, are so good for children with ADHD. "They learn how to interact with people, adapt to an environment and judge situations - skills that will help in real life. "Boxing, especially, teaches impulse control and channels emotions in a constructive way. "People with ADHD have so many positives. They are often fun, creative and engaging. They might simply need help to channel their energy and sport is great way to do this." What is Attention deficit hyperactivity disorder (ADHD)?

Here is an summary requirement:
Describe the quoted teenagers suffering from ADHD and their ages.

Please summarize the above article based on the given requirement:

# Output (a):
The article features three teenagers with ADHD who have been excluded from mainstream school. Levi, 14, and Aalijah, 14, have found boxing to be a positive outlet for their excess energy and difficulty concentrating. Jamie, 11, also struggles with anger related to his ADHD, but has found that boxing helps him control it.

# Output (b):
The article discusses how teenagers with ADHD are finding relief and success through boxing. 14-year-old Levi and 14-year-old Aalijah have both been diagnosed with ADHD and have struggled in school as a result. However, they have found that boxing helps them to channel their energy and focus better. 11-year-old Jamie has also found that boxing helps him to control his anger.

# Which is better, Output (a) or Output (b)? Your response should be either "Output (a)" or "Output (b)":

Output (a)

Instruction:
Here is an article:
These teenagers all have ADHD and have been excluded from mainstream school. Here they tell how the sport of boxing is helping them channel their excess energy and improve their life skills. By Gwyneth ReesBBC News In an amateur boxing gym in Barry, 14-year-old Levi is tugging on his boxing gloves, ready to join his friends in training. Some are already practising in the ring, aiming jabs and uppercuts. Others are punching the bags or skipping as music plays in the background and the smell of sweat and chalk permeates the air. Levi is a fighter. He always has been. In fact, he has been excluded from school twice for disobedient behaviour. But now he's using the discipline of boxing to channel his fighting spirit in a more positive way. Every Thursday, he partakes in a two-hour non-contact boxing session at Colcot Amateur Boxing Club, designed to help pupils who have been excluded from mainstream school. "I used to lose my temper easily," Levi explains. "I started a lot of fights and used to mess up the classrooms. "I used to be really disrespectful to the teachers and felt so down all the time, thinking I was just a naughty kid." Once excluded, however, and sent to a pupil referral unit in the Vale of Glamorgan, Levi got diagnosed with attention deficit hyperactivity disorder (ADHD) - a condition which causes inattentiveness, impulsiveness and an inability to stick to tasks. It is also closely linked with underachievement at school, poor social interaction and problems with discipline. "The diagnosis and medication helped," he explains. "But it's the boxing that really helps as I'm always so full of energy and struggle to concentrate. "I get excited and want to train my best. "Anthony Joshua is my role model and I want to follow in his steps. "But my main aim now is just to get my head down and get my GCSEs." It is a similar story for Aalijah, 14. She too has ADHD and her inability to concentrate meant she fell behind academically from a young age, causing her behaviour to spiral. Frustrated with school and feeling like she constantly had to "catch up", she was eventually excluded for intimidating her teachers. "I don't like school," she explains. "It puts me under pressure and frustrates me. "If I try to concentrate on something, my mind is just blank… and with the ADHD, I can't control what I do. It's like my body is locked back and in its own trance." But if Aalijah struggled with school, in boxing she has found something she loves. "The boxing really helps," she explains. "It lowers my energy and calms me down, helping me to concentrate. "After it, I feel a lot calmer and more sensible." Aalijah and Levi are just two of the teenagers helped by the weekly boxing sessions. Run by Empire Fighting Chance, a Bristol-based charity that launched in south Wales in 2016, the classes are designed to teach confidence and resilience, improving the life chances of those involved. It is now expanding across the whole of Wales, with sessions due to begin in Rhyl, north Wales, and other locations by the end of the year. Explaining the benefits, coach Nathan Powell, says: "Many of the kids we see have anxiety and confidence issues and struggle to work with others. "At the start of the sessions, team activities always end in arguments and confrontation. "But with work and drills, their confidence grows and they become a member of a team. These kids are also used to failing, so we break things down into mini goals, like building up the number of skips they can do. "This helps them reach a target and achieve something. It's very important and makes them feel good about themselves." A third pupil, Jamie, 11, got excluded from primary school when he was just nine despite teachers knowing about his diagnosis. "Anger is a big part of my ADHD," he says. "If I get annoyed it takes me a long time to calm down. "But the boxing helps me get my anger out and it helps me control it." As for Jamie Parry, head of business development for Empire Fighting Chance, he believes that mainstream education might not be for everyone. "Many children need a different way of learning," he says. "We try to boost aspiration by providing good role models in the form of our coaches, and we drop in personal development tips about nutrition and sleep. "The pupils often see teachers as a negative sign of authority so we try to build trust with them." According to statistics from the UK ADHD Partnership, children with ADHD have more than 100 times greater risk of being permanently excluded from school than other children. Roughly 40% of children with ADHD have had fixed-term exclusions from school and 11% have been permanently excluded. Susan Young, president of the UK ADHD Partnership, said: "Sporting activities, such as boxing, are so good for children with ADHD. "They learn how to interact with people, adapt to an environment and judge situations - skills that will help in real life. "Boxing, especially, teaches impulse control and channels emotions in a constructive way. "People with ADHD have so many positives. They are often fun, creative and engaging. They might simply need help to channel their energy and sport is great way to do this." What is Attention deficit hyperactivity disorder (ADHD)?

Here is an summary requirement:
Describe the quoted teenagers suffering from ADHD and their ages.

Please summarize the above article based on the given requirement:

The article features three teenagers with ADHD who have been excluded from mainstream school. Levi, 14, and Aalijah, 14, have found boxing to be a positive outlet for their excess energy and difficulty concentrating. Jamie, 11, also struggles with anger related to his ADHD, but has found that boxing helps him control it.

The article discusses how teenagers with ADHD are finding relief and success through boxing. 14-year-old Levi and 14-year-old Aalijah have both been diagnosed with ADHD and have struggled in school as a result. However, they have found that boxing helps them to channel their energy and focus better. 11-year-old Jamie has also found that boxing helps him to control his anger.


You are a helpful assistant in evaluating the quality of the outputs for a given instruction. Your goal is to select the best output for the given instruction.

Select the Output (a) or Output (b) that is better for the given instruction. The two outputs are generated by two different AI chatbots respectively.

Here are some rules of the evaluation:
(1) You should prioritize evaluating whether the output honestly/precisely/closely executes the instruction, then consider its helpfulness, accuracy, level of detail, harmlessness, etc.
(2) Outputs should NOT contain more/less than what the instruction asks for, as such outputs do NOT precisely execute the instruction.
(3) You should avoid any potential bias and your judgment should be as objective as possible. For example, the order in which the outputs were presented should NOT affect your judgment, as Output (a) and Output (b) are **equally likely** to be the better.

Do NOT provide any explanation for your choice.
Do NOT say both / neither are good.
You should answer using ONLY "Output (a)" or "Output (b)". Do NOT output any other words.

# Instruction:
Here is an article:
Thousands of students in England are angry about the controversial use of an algorithm to determine this year's GCSE and A-level results. By James Clayton and Zoe KleinmanTechnology reporters, BBC News They were unable to sit exams because of lockdown, so the algorithm used data about schools' results in previous years to determine grades. It meant about 40% of this year's A-level results came out lower than predicted, which has a huge impact on what students are able to do next. GCSE results are due out on Thursday. There are many examples of algorithms making big decisions about our lives, without us necessarily knowing how or when they do it. Here's a look at some of them. Social media In many ways, social-media platforms are simply giant algorithms. At their heart, they work out what you're interested in and then give you more of it - using as many data points as they can get their hands on. Every "like", watch, click is stored. Most apps also glean more data from your web-browsing habits or geographical data. The idea is to predict the content you want and keep you scrolling - and it works. And those same algorithms that know you enjoy a cute-cat video are also deployed to sell you stuff. All the data social-media companies collect about you can also tailor ads to you in an incredibly accurate way. But these algorithms can go seriously wrong. They have been proved to push people towards hateful and extremist content. Extreme content simply does better than nuance on social media. And algorithms know that. Facebook's own civil-rights audit called for the company to do everything in its power to prevent its algorithm from "driving people toward self-reinforcing echo chambers of extremism". And last month we reported on how algorithms on online retail sites - designed to work out what you want to buy - were pushing racist and hateful products. Insurance Whether it's house, car, health or any other form of insurance, your insurer has to somehow assess the chances of something actually going wrong. In many ways, the insurance industry pioneered using data about the past to determine future outcomes - that's the basis of the whole sector, according to Timandra Harkness, author of Big Data: Does Size Matter. Getting a computer to do it was always going to be the logical next step. "Algorithms can affect your life very much and yet you as an individual don't necessarily get a lot of input," she says. "We all know if you move to a different postcode, your insurance goes up or down. "That's not because of you, it's because other people have been more or less likely to have been victims of crime, or had accidents or whatever." Innovations such as the "black box" that can be installed in a car to monitor how an individual drives have helped to lower the cost of car insurance for careful drivers who find themselves in a high-risk group. Might we see more personally tailored insurance quotes as algorithms learn more about our own circumstances? "Ultimately the point of insurance is to share the risk - so everybody puts [money] in and the people who need it take it out," Timandra says. "We live in an unfair world, so any model you make is going to be unfair in one way or another." Healthcare Artificial Intelligence is making great leaps in being able to diagnose various conditions and even suggest treatment paths. A study published in January 2020 suggested an algorithm performed better than human doctors when it came to identifying breast cancer from mammograms. And other successes include: However, all this requires a vast amount of patient data to train the programmes - and that is, frankly, a rather large can of worms. In 2017, the UK Information Commission ruled the Royal Free NHS Foundation Trust had not done enough to safeguard patient data when it had shared 1.6 million patient records with Google's AI division, DeepMind. "There's a fine line between finding exciting new ways to improve care and moving ahead of patients' expectations," said DeepMind's co-founder Mustafa Suleyman at the time. Policing Big data and machine learning have the potential to revolutionise policing. In theory, algorithms have the power to deliver on the sci-fi promise of "predictive policing" - using data, such as where crime has happened in the past, when and by whom, to predict where to allocate police resources. But that method can create algorithmic bias - and even algorithmic racism. "It's the same situation as you have with the exam grades," says Areeq Chowdhury, from technology think tank WebRoots Democracy. "Why are you judging one individual based on what other people have historically done? The same communities are always over-represented". Earlier this year, the defence and security think tank RUSI published a report into algorithmic policing. It raised concerns about the lack of national guidelines or impact assessments. It also called for more research into how these algorithms might exacerbate racism. Facial recognition too - used by police forces in the UK including the Met - has also been criticised. For example, there have been concerns about whether the data going into facial-recognition technology can make the algorithm racist. The charge is facial-recognition cameras are more accurate at identifying white faces - because they have more data on white faces. "The question is, are you testing it on a diverse enough demographic of people?" Areeq says. "What you don't want is a situation where some groups are being misidentified as a criminal because of the algorithm."

Here is an summary requirement:
Summarize the list of examples of algorithms making big decisions about our lives.

Please summarize the above article based on the given requirement:

# Output (a):
The article lists several examples of algorithms making big decisions about our lives, including social media, insurance, healthcare, and policing. In each case, the algorithm can have a significant impact on individuals, but there is often little transparency about how the algorithm works or how it is making decisions. This can lead to problems such as bias, racism, and unfairness.

# Output (b):
Algorithms are being used to make big decisions about our lives in many ways, such as on social media platforms, in the insurance industry, in healthcare, and in policing. Social media algorithms work out what users are interested in and give them more of it, while insurance algorithms use data about the past to determine future outcomes. AI is being used to diagnose conditions and suggest treatment paths in healthcare, while predictive policing uses data to allocate police resources. Facial recognition technology has also been criticized for potentially exacerbating racism.

# Which is better, Output (a) or Output (b)? Your response should be either "Output (a)" or "Output (b)":

Output (b)

Instruction:
Here is an article:
Thousands of students in England are angry about the controversial use of an algorithm to determine this year's GCSE and A-level results. By James Clayton and Zoe KleinmanTechnology reporters, BBC News They were unable to sit exams because of lockdown, so the algorithm used data about schools' results in previous years to determine grades. It meant about 40% of this year's A-level results came out lower than predicted, which has a huge impact on what students are able to do next. GCSE results are due out on Thursday. There are many examples of algorithms making big decisions about our lives, without us necessarily knowing how or when they do it. Here's a look at some of them. Social media In many ways, social-media platforms are simply giant algorithms. At their heart, they work out what you're interested in and then give you more of it - using as many data points as they can get their hands on. Every "like", watch, click is stored. Most apps also glean more data from your web-browsing habits or geographical data. The idea is to predict the content you want and keep you scrolling - and it works. And those same algorithms that know you enjoy a cute-cat video are also deployed to sell you stuff. All the data social-media companies collect about you can also tailor ads to you in an incredibly accurate way. But these algorithms can go seriously wrong. They have been proved to push people towards hateful and extremist content. Extreme content simply does better than nuance on social media. And algorithms know that. Facebook's own civil-rights audit called for the company to do everything in its power to prevent its algorithm from "driving people toward self-reinforcing echo chambers of extremism". And last month we reported on how algorithms on online retail sites - designed to work out what you want to buy - were pushing racist and hateful products. Insurance Whether it's house, car, health or any other form of insurance, your insurer has to somehow assess the chances of something actually going wrong. In many ways, the insurance industry pioneered using data about the past to determine future outcomes - that's the basis of the whole sector, according to Timandra Harkness, author of Big Data: Does Size Matter. Getting a computer to do it was always going to be the logical next step. "Algorithms can affect your life very much and yet you as an individual don't necessarily get a lot of input," she says. "We all know if you move to a different postcode, your insurance goes up or down. "That's not because of you, it's because other people have been more or less likely to have been victims of crime, or had accidents or whatever." Innovations such as the "black box" that can be installed in a car to monitor how an individual drives have helped to lower the cost of car insurance for careful drivers who find themselves in a high-risk group. Might we see more personally tailored insurance quotes as algorithms learn more about our own circumstances? "Ultimately the point of insurance is to share the risk - so everybody puts [money] in and the people who need it take it out," Timandra says. "We live in an unfair world, so any model you make is going to be unfair in one way or another." Healthcare Artificial Intelligence is making great leaps in being able to diagnose various conditions and even suggest treatment paths. A study published in January 2020 suggested an algorithm performed better than human doctors when it came to identifying breast cancer from mammograms. And other successes include: However, all this requires a vast amount of patient data to train the programmes - and that is, frankly, a rather large can of worms. In 2017, the UK Information Commission ruled the Royal Free NHS Foundation Trust had not done enough to safeguard patient data when it had shared 1.6 million patient records with Google's AI division, DeepMind. "There's a fine line between finding exciting new ways to improve care and moving ahead of patients' expectations," said DeepMind's co-founder Mustafa Suleyman at the time. Policing Big data and machine learning have the potential to revolutionise policing. In theory, algorithms have the power to deliver on the sci-fi promise of "predictive policing" - using data, such as where crime has happened in the past, when and by whom, to predict where to allocate police resources. But that method can create algorithmic bias - and even algorithmic racism. "It's the same situation as you have with the exam grades," says Areeq Chowdhury, from technology think tank WebRoots Democracy. "Why are you judging one individual based on what other people have historically done? The same communities are always over-represented". Earlier this year, the defence and security think tank RUSI published a report into algorithmic policing. It raised concerns about the lack of national guidelines or impact assessments. It also called for more research into how these algorithms might exacerbate racism. Facial recognition too - used by police forces in the UK including the Met - has also been criticised. For example, there have been concerns about whether the data going into facial-recognition technology can make the algorithm racist. The charge is facial-recognition cameras are more accurate at identifying white faces - because they have more data on white faces. "The question is, are you testing it on a diverse enough demographic of people?" Areeq says. "What you don't want is a situation where some groups are being misidentified as a criminal because of the algorithm."

Here is an summary requirement:
Summarize the list of examples of algorithms making big decisions about our lives.

Please summarize the above article based on the given requirement:

The article lists several examples of algorithms making big decisions about our lives, including social media, insurance, healthcare, and policing. In each case, the algorithm can have a significant impact on individuals, but there is often little transparency about how the algorithm works or how it is making decisions. This can lead to problems such as bias, racism, and unfairness.

Algorithms are being used to make big decisions about our lives in many ways, such as on social media platforms, in the insurance industry, in healthcare, and in policing. Social media algorithms work out what users are interested in and give them more of it, while insurance algorithms use data about the past to determine future outcomes. AI is being used to diagnose conditions and suggest treatment paths in healthcare, while predictive policing uses data to allocate police resources. Facial recognition technology has also been criticized for potentially exacerbating racism.


You are a helpful assistant in evaluating the quality of the outputs for a given instruction. Your goal is to select the best output for the given instruction.

Select the Output (a) or Output (b) that is better for the given instruction. The two outputs are generated by two different AI chatbots respectively.

Here are some rules of the evaluation:
(1) You should prioritize evaluating whether the output honestly/precisely/closely executes the instruction, then consider its helpfulness, accuracy, level of detail, harmlessness, etc.
(2) Outputs should NOT contain more/less than what the instruction asks for, as such outputs do NOT precisely execute the instruction.
(3) You should avoid any potential bias and your judgment should be as objective as possible. For example, the order in which the outputs were presented should NOT affect your judgment, as Output (a) and Output (b) are **equally likely** to be the better.

Do NOT provide any explanation for your choice.
Do NOT say both / neither are good.
You should answer using ONLY "Output (a)" or "Output (b)". Do NOT output any other words.

# Instruction:
Here is an article:
Thousands of students in England are angry about the controversial use of an algorithm to determine this year's GCSE and A-level results. By James Clayton and Zoe KleinmanTechnology reporters, BBC News They were unable to sit exams because of lockdown, so the algorithm used data about schools' results in previous years to determine grades. It meant about 40% of this year's A-level results came out lower than predicted, which has a huge impact on what students are able to do next. GCSE results are due out on Thursday. There are many examples of algorithms making big decisions about our lives, without us necessarily knowing how or when they do it. Here's a look at some of them. Social media In many ways, social-media platforms are simply giant algorithms. At their heart, they work out what you're interested in and then give you more of it - using as many data points as they can get their hands on. Every "like", watch, click is stored. Most apps also glean more data from your web-browsing habits or geographical data. The idea is to predict the content you want and keep you scrolling - and it works. And those same algorithms that know you enjoy a cute-cat video are also deployed to sell you stuff. All the data social-media companies collect about you can also tailor ads to you in an incredibly accurate way. But these algorithms can go seriously wrong. They have been proved to push people towards hateful and extremist content. Extreme content simply does better than nuance on social media. And algorithms know that. Facebook's own civil-rights audit called for the company to do everything in its power to prevent its algorithm from "driving people toward self-reinforcing echo chambers of extremism". And last month we reported on how algorithms on online retail sites - designed to work out what you want to buy - were pushing racist and hateful products. Insurance Whether it's house, car, health or any other form of insurance, your insurer has to somehow assess the chances of something actually going wrong. In many ways, the insurance industry pioneered using data about the past to determine future outcomes - that's the basis of the whole sector, according to Timandra Harkness, author of Big Data: Does Size Matter. Getting a computer to do it was always going to be the logical next step. "Algorithms can affect your life very much and yet you as an individual don't necessarily get a lot of input," she says. "We all know if you move to a different postcode, your insurance goes up or down. "That's not because of you, it's because other people have been more or less likely to have been victims of crime, or had accidents or whatever." Innovations such as the "black box" that can be installed in a car to monitor how an individual drives have helped to lower the cost of car insurance for careful drivers who find themselves in a high-risk group. Might we see more personally tailored insurance quotes as algorithms learn more about our own circumstances? "Ultimately the point of insurance is to share the risk - so everybody puts [money] in and the people who need it take it out," Timandra says. "We live in an unfair world, so any model you make is going to be unfair in one way or another." Healthcare Artificial Intelligence is making great leaps in being able to diagnose various conditions and even suggest treatment paths. A study published in January 2020 suggested an algorithm performed better than human doctors when it came to identifying breast cancer from mammograms. And other successes include: However, all this requires a vast amount of patient data to train the programmes - and that is, frankly, a rather large can of worms. In 2017, the UK Information Commission ruled the Royal Free NHS Foundation Trust had not done enough to safeguard patient data when it had shared 1.6 million patient records with Google's AI division, DeepMind. "There's a fine line between finding exciting new ways to improve care and moving ahead of patients' expectations," said DeepMind's co-founder Mustafa Suleyman at the time. Policing Big data and machine learning have the potential to revolutionise policing. In theory, algorithms have the power to deliver on the sci-fi promise of "predictive policing" - using data, such as where crime has happened in the past, when and by whom, to predict where to allocate police resources. But that method can create algorithmic bias - and even algorithmic racism. "It's the same situation as you have with the exam grades," says Areeq Chowdhury, from technology think tank WebRoots Democracy. "Why are you judging one individual based on what other people have historically done? The same communities are always over-represented". Earlier this year, the defence and security think tank RUSI published a report into algorithmic policing. It raised concerns about the lack of national guidelines or impact assessments. It also called for more research into how these algorithms might exacerbate racism. Facial recognition too - used by police forces in the UK including the Met - has also been criticised. For example, there have been concerns about whether the data going into facial-recognition technology can make the algorithm racist. The charge is facial-recognition cameras are more accurate at identifying white faces - because they have more data on white faces. "The question is, are you testing it on a diverse enough demographic of people?" Areeq says. "What you don't want is a situation where some groups are being misidentified as a criminal because of the algorithm."

Here is an summary requirement:
Summarize the list of examples of algorithms making big decisions about our lives.

Please summarize the above article based on the given requirement:

# Output (a):
The article discusses how algorithms are being used to make big decisions in various aspects of our lives. Social media platforms use algorithms to predict and provide content that users are interested in, but these algorithms can also push people towards extremist content. Insurance companies use algorithms to assess the chances of something going wrong and determine insurance quotes. Healthcare is also using algorithms to diagnose and suggest treatment paths, but this requires a vast amount of patient data to train the programs. Policing is another area where algorithms have the potential to revolutionize policing, but there are concerns about algorithmic bias and racism. Facial recognition technology has also been criticized for being more accurate at identifying white faces, leading to misidentification of certain groups as criminals.

# Output (b):
The article lists several examples of algorithms making big decisions about our lives, including social media, insurance, healthcare, and policing. In each case, the algorithm can have a significant impact on individuals, but there is often little transparency about how the algorithm works or how it is making decisions. This can lead to problems such as bias, racism, and unfairness.

# Which is better, Output (a) or Output (b)? Your response should be either "Output (a)" or "Output (b)":

Output (a)

Instruction:
Here is an article:
Thousands of students in England are angry about the controversial use of an algorithm to determine this year's GCSE and A-level results. By James Clayton and Zoe KleinmanTechnology reporters, BBC News They were unable to sit exams because of lockdown, so the algorithm used data about schools' results in previous years to determine grades. It meant about 40% of this year's A-level results came out lower than predicted, which has a huge impact on what students are able to do next. GCSE results are due out on Thursday. There are many examples of algorithms making big decisions about our lives, without us necessarily knowing how or when they do it. Here's a look at some of them. Social media In many ways, social-media platforms are simply giant algorithms. At their heart, they work out what you're interested in and then give you more of it - using as many data points as they can get their hands on. Every "like", watch, click is stored. Most apps also glean more data from your web-browsing habits or geographical data. The idea is to predict the content you want and keep you scrolling - and it works. And those same algorithms that know you enjoy a cute-cat video are also deployed to sell you stuff. All the data social-media companies collect about you can also tailor ads to you in an incredibly accurate way. But these algorithms can go seriously wrong. They have been proved to push people towards hateful and extremist content. Extreme content simply does better than nuance on social media. And algorithms know that. Facebook's own civil-rights audit called for the company to do everything in its power to prevent its algorithm from "driving people toward self-reinforcing echo chambers of extremism". And last month we reported on how algorithms on online retail sites - designed to work out what you want to buy - were pushing racist and hateful products. Insurance Whether it's house, car, health or any other form of insurance, your insurer has to somehow assess the chances of something actually going wrong. In many ways, the insurance industry pioneered using data about the past to determine future outcomes - that's the basis of the whole sector, according to Timandra Harkness, author of Big Data: Does Size Matter. Getting a computer to do it was always going to be the logical next step. "Algorithms can affect your life very much and yet you as an individual don't necessarily get a lot of input," she says. "We all know if you move to a different postcode, your insurance goes up or down. "That's not because of you, it's because other people have been more or less likely to have been victims of crime, or had accidents or whatever." Innovations such as the "black box" that can be installed in a car to monitor how an individual drives have helped to lower the cost of car insurance for careful drivers who find themselves in a high-risk group. Might we see more personally tailored insurance quotes as algorithms learn more about our own circumstances? "Ultimately the point of insurance is to share the risk - so everybody puts [money] in and the people who need it take it out," Timandra says. "We live in an unfair world, so any model you make is going to be unfair in one way or another." Healthcare Artificial Intelligence is making great leaps in being able to diagnose various conditions and even suggest treatment paths. A study published in January 2020 suggested an algorithm performed better than human doctors when it came to identifying breast cancer from mammograms. And other successes include: However, all this requires a vast amount of patient data to train the programmes - and that is, frankly, a rather large can of worms. In 2017, the UK Information Commission ruled the Royal Free NHS Foundation Trust had not done enough to safeguard patient data when it had shared 1.6 million patient records with Google's AI division, DeepMind. "There's a fine line between finding exciting new ways to improve care and moving ahead of patients' expectations," said DeepMind's co-founder Mustafa Suleyman at the time. Policing Big data and machine learning have the potential to revolutionise policing. In theory, algorithms have the power to deliver on the sci-fi promise of "predictive policing" - using data, such as where crime has happened in the past, when and by whom, to predict where to allocate police resources. But that method can create algorithmic bias - and even algorithmic racism. "It's the same situation as you have with the exam grades," says Areeq Chowdhury, from technology think tank WebRoots Democracy. "Why are you judging one individual based on what other people have historically done? The same communities are always over-represented". Earlier this year, the defence and security think tank RUSI published a report into algorithmic policing. It raised concerns about the lack of national guidelines or impact assessments. It also called for more research into how these algorithms might exacerbate racism. Facial recognition too - used by police forces in the UK including the Met - has also been criticised. For example, there have been concerns about whether the data going into facial-recognition technology can make the algorithm racist. The charge is facial-recognition cameras are more accurate at identifying white faces - because they have more data on white faces. "The question is, are you testing it on a diverse enough demographic of people?" Areeq says. "What you don't want is a situation where some groups are being misidentified as a criminal because of the algorithm."

Here is an summary requirement:
Summarize the list of examples of algorithms making big decisions about our lives.

Please summarize the above article based on the given requirement:

The article discusses how algorithms are being used to make big decisions in various aspects of our lives. Social media platforms use algorithms to predict and provide content that users are interested in, but these algorithms can also push people towards extremist content. Insurance companies use algorithms to assess the chances of something going wrong and determine insurance quotes. Healthcare is also using algorithms to diagnose and suggest treatment paths, but this requires a vast amount of patient data to train the programs. Policing is another area where algorithms have the potential to revolutionize policing, but there are concerns about algorithmic bias and racism. Facial recognition technology has also been criticized for being more accurate at identifying white faces, leading to misidentification of certain groups as criminals.

The article lists several examples of algorithms making big decisions about our lives, including social media, insurance, healthcare, and policing. In each case, the algorithm can have a significant impact on individuals, but there is often little transparency about how the algorithm works or how it is making decisions. This can lead to problems such as bias, racism, and unfairness.


You are a helpful assistant in evaluating the quality of the outputs for a given instruction. Your goal is to select the best output for the given instruction.

Select the Output (a) or Output (b) that is better for the given instruction. The two outputs are generated by two different AI chatbots respectively.

Here are some rules of the evaluation:
(1) You should prioritize evaluating whether the output honestly/precisely/closely executes the instruction, then consider its helpfulness, accuracy, level of detail, harmlessness, etc.
(2) Outputs should NOT contain more/less than what the instruction asks for, as such outputs do NOT precisely execute the instruction.
(3) You should avoid any potential bias and your judgment should be as objective as possible. For example, the order in which the outputs were presented should NOT affect your judgment, as Output (a) and Output (b) are **equally likely** to be the better.

Do NOT provide any explanation for your choice.
Do NOT say both / neither are good.
You should answer using ONLY "Output (a)" or "Output (b)". Do NOT output any other words.

# Instruction:
Here is an article:
Thousands of students in England are angry about the controversial use of an algorithm to determine this year's GCSE and A-level results. By James Clayton and Zoe KleinmanTechnology reporters, BBC News They were unable to sit exams because of lockdown, so the algorithm used data about schools' results in previous years to determine grades. It meant about 40% of this year's A-level results came out lower than predicted, which has a huge impact on what students are able to do next. GCSE results are due out on Thursday. There are many examples of algorithms making big decisions about our lives, without us necessarily knowing how or when they do it. Here's a look at some of them. Social media In many ways, social-media platforms are simply giant algorithms. At their heart, they work out what you're interested in and then give you more of it - using as many data points as they can get their hands on. Every "like", watch, click is stored. Most apps also glean more data from your web-browsing habits or geographical data. The idea is to predict the content you want and keep you scrolling - and it works. And those same algorithms that know you enjoy a cute-cat video are also deployed to sell you stuff. All the data social-media companies collect about you can also tailor ads to you in an incredibly accurate way. But these algorithms can go seriously wrong. They have been proved to push people towards hateful and extremist content. Extreme content simply does better than nuance on social media. And algorithms know that. Facebook's own civil-rights audit called for the company to do everything in its power to prevent its algorithm from "driving people toward self-reinforcing echo chambers of extremism". And last month we reported on how algorithms on online retail sites - designed to work out what you want to buy - were pushing racist and hateful products. Insurance Whether it's house, car, health or any other form of insurance, your insurer has to somehow assess the chances of something actually going wrong. In many ways, the insurance industry pioneered using data about the past to determine future outcomes - that's the basis of the whole sector, according to Timandra Harkness, author of Big Data: Does Size Matter. Getting a computer to do it was always going to be the logical next step. "Algorithms can affect your life very much and yet you as an individual don't necessarily get a lot of input," she says. "We all know if you move to a different postcode, your insurance goes up or down. "That's not because of you, it's because other people have been more or less likely to have been victims of crime, or had accidents or whatever." Innovations such as the "black box" that can be installed in a car to monitor how an individual drives have helped to lower the cost of car insurance for careful drivers who find themselves in a high-risk group. Might we see more personally tailored insurance quotes as algorithms learn more about our own circumstances? "Ultimately the point of insurance is to share the risk - so everybody puts [money] in and the people who need it take it out," Timandra says. "We live in an unfair world, so any model you make is going to be unfair in one way or another." Healthcare Artificial Intelligence is making great leaps in being able to diagnose various conditions and even suggest treatment paths. A study published in January 2020 suggested an algorithm performed better than human doctors when it came to identifying breast cancer from mammograms. And other successes include: However, all this requires a vast amount of patient data to train the programmes - and that is, frankly, a rather large can of worms. In 2017, the UK Information Commission ruled the Royal Free NHS Foundation Trust had not done enough to safeguard patient data when it had shared 1.6 million patient records with Google's AI division, DeepMind. "There's a fine line between finding exciting new ways to improve care and moving ahead of patients' expectations," said DeepMind's co-founder Mustafa Suleyman at the time. Policing Big data and machine learning have the potential to revolutionise policing. In theory, algorithms have the power to deliver on the sci-fi promise of "predictive policing" - using data, such as where crime has happened in the past, when and by whom, to predict where to allocate police resources. But that method can create algorithmic bias - and even algorithmic racism. "It's the same situation as you have with the exam grades," says Areeq Chowdhury, from technology think tank WebRoots Democracy. "Why are you judging one individual based on what other people have historically done? The same communities are always over-represented". Earlier this year, the defence and security think tank RUSI published a report into algorithmic policing. It raised concerns about the lack of national guidelines or impact assessments. It also called for more research into how these algorithms might exacerbate racism. Facial recognition too - used by police forces in the UK including the Met - has also been criticised. For example, there have been concerns about whether the data going into facial-recognition technology can make the algorithm racist. The charge is facial-recognition cameras are more accurate at identifying white faces - because they have more data on white faces. "The question is, are you testing it on a diverse enough demographic of people?" Areeq says. "What you don't want is a situation where some groups are being misidentified as a criminal because of the algorithm."

Here is an summary requirement:
Summarize the list of examples of algorithms making big decisions about our lives.

Please summarize the above article based on the given requirement:

# Output (a):
Algorithms play a significant role in various aspects of our lives, including social media, insurance, healthcare, and policing. In social media, they determine the content we see and the ads we receive based on our online behavior. In insurance, they assess risks and determine premiums based on historical data and individual factors. In healthcare, AI algorithms can diagnose conditions and suggest treatment paths, but require vast amounts of patient data. In policing, algorithms have the potential to revolutionize resource allocation and predictive policing, but can also lead to algorithmic bias and racism.

# Output (b):
The article lists several examples of algorithms making big decisions about our lives, including social media, insurance, healthcare, and policing. In each case, the algorithm can have a significant impact on individuals, but there is often little transparency about how the algorithm works or how it is making decisions. This can lead to problems such as bias, racism, and unfairness.

# Which is better, Output (a) or Output (b)? Your response should be either "Output (a)" or "Output (b)":

Output (a)

Instruction:
Here is an article:
Thousands of students in England are angry about the controversial use of an algorithm to determine this year's GCSE and A-level results. By James Clayton and Zoe KleinmanTechnology reporters, BBC News They were unable to sit exams because of lockdown, so the algorithm used data about schools' results in previous years to determine grades. It meant about 40% of this year's A-level results came out lower than predicted, which has a huge impact on what students are able to do next. GCSE results are due out on Thursday. There are many examples of algorithms making big decisions about our lives, without us necessarily knowing how or when they do it. Here's a look at some of them. Social media In many ways, social-media platforms are simply giant algorithms. At their heart, they work out what you're interested in and then give you more of it - using as many data points as they can get their hands on. Every "like", watch, click is stored. Most apps also glean more data from your web-browsing habits or geographical data. The idea is to predict the content you want and keep you scrolling - and it works. And those same algorithms that know you enjoy a cute-cat video are also deployed to sell you stuff. All the data social-media companies collect about you can also tailor ads to you in an incredibly accurate way. But these algorithms can go seriously wrong. They have been proved to push people towards hateful and extremist content. Extreme content simply does better than nuance on social media. And algorithms know that. Facebook's own civil-rights audit called for the company to do everything in its power to prevent its algorithm from "driving people toward self-reinforcing echo chambers of extremism". And last month we reported on how algorithms on online retail sites - designed to work out what you want to buy - were pushing racist and hateful products. Insurance Whether it's house, car, health or any other form of insurance, your insurer has to somehow assess the chances of something actually going wrong. In many ways, the insurance industry pioneered using data about the past to determine future outcomes - that's the basis of the whole sector, according to Timandra Harkness, author of Big Data: Does Size Matter. Getting a computer to do it was always going to be the logical next step. "Algorithms can affect your life very much and yet you as an individual don't necessarily get a lot of input," she says. "We all know if you move to a different postcode, your insurance goes up or down. "That's not because of you, it's because other people have been more or less likely to have been victims of crime, or had accidents or whatever." Innovations such as the "black box" that can be installed in a car to monitor how an individual drives have helped to lower the cost of car insurance for careful drivers who find themselves in a high-risk group. Might we see more personally tailored insurance quotes as algorithms learn more about our own circumstances? "Ultimately the point of insurance is to share the risk - so everybody puts [money] in and the people who need it take it out," Timandra says. "We live in an unfair world, so any model you make is going to be unfair in one way or another." Healthcare Artificial Intelligence is making great leaps in being able to diagnose various conditions and even suggest treatment paths. A study published in January 2020 suggested an algorithm performed better than human doctors when it came to identifying breast cancer from mammograms. And other successes include: However, all this requires a vast amount of patient data to train the programmes - and that is, frankly, a rather large can of worms. In 2017, the UK Information Commission ruled the Royal Free NHS Foundation Trust had not done enough to safeguard patient data when it had shared 1.6 million patient records with Google's AI division, DeepMind. "There's a fine line between finding exciting new ways to improve care and moving ahead of patients' expectations," said DeepMind's co-founder Mustafa Suleyman at the time. Policing Big data and machine learning have the potential to revolutionise policing. In theory, algorithms have the power to deliver on the sci-fi promise of "predictive policing" - using data, such as where crime has happened in the past, when and by whom, to predict where to allocate police resources. But that method can create algorithmic bias - and even algorithmic racism. "It's the same situation as you have with the exam grades," says Areeq Chowdhury, from technology think tank WebRoots Democracy. "Why are you judging one individual based on what other people have historically done? The same communities are always over-represented". Earlier this year, the defence and security think tank RUSI published a report into algorithmic policing. It raised concerns about the lack of national guidelines or impact assessments. It also called for more research into how these algorithms might exacerbate racism. Facial recognition too - used by police forces in the UK including the Met - has also been criticised. For example, there have been concerns about whether the data going into facial-recognition technology can make the algorithm racist. The charge is facial-recognition cameras are more accurate at identifying white faces - because they have more data on white faces. "The question is, are you testing it on a diverse enough demographic of people?" Areeq says. "What you don't want is a situation where some groups are being misidentified as a criminal because of the algorithm."

Here is an summary requirement:
Summarize the list of examples of algorithms making big decisions about our lives.

Please summarize the above article based on the given requirement:

Algorithms play a significant role in various aspects of our lives, including social media, insurance, healthcare, and policing. In social media, they determine the content we see and the ads we receive based on our online behavior. In insurance, they assess risks and determine premiums based on historical data and individual factors. In healthcare, AI algorithms can diagnose conditions and suggest treatment paths, but require vast amounts of patient data. In policing, algorithms have the potential to revolutionize resource allocation and predictive policing, but can also lead to algorithmic bias and racism.

The article lists several examples of algorithms making big decisions about our lives, including social media, insurance, healthcare, and policing. In each case, the algorithm can have a significant impact on individuals, but there is often little transparency about how the algorithm works or how it is making decisions. This can lead to problems such as bias, racism, and unfairness.


You are a helpful assistant in evaluating the quality of the outputs for a given instruction. Your goal is to select the best output for the given instruction.

Select the Output (a) or Output (b) that is better for the given instruction. The two outputs are generated by two different AI chatbots respectively.

Here are some rules of the evaluation:
(1) You should prioritize evaluating whether the output honestly/precisely/closely executes the instruction, then consider its helpfulness, accuracy, level of detail, harmlessness, etc.
(2) Outputs should NOT contain more/less than what the instruction asks for, as such outputs do NOT precisely execute the instruction.
(3) You should avoid any potential bias and your judgment should be as objective as possible. For example, the order in which the outputs were presented should NOT affect your judgment, as Output (a) and Output (b) are **equally likely** to be the better.

Do NOT provide any explanation for your choice.
Do NOT say both / neither are good.
You should answer using ONLY "Output (a)" or "Output (b)". Do NOT output any other words.

# Instruction:
Here is an article:
Thousands of students in England are angry about the controversial use of an algorithm to determine this year's GCSE and A-level results. By James Clayton and Zoe KleinmanTechnology reporters, BBC News They were unable to sit exams because of lockdown, so the algorithm used data about schools' results in previous years to determine grades. It meant about 40% of this year's A-level results came out lower than predicted, which has a huge impact on what students are able to do next. GCSE results are due out on Thursday. There are many examples of algorithms making big decisions about our lives, without us necessarily knowing how or when they do it. Here's a look at some of them. Social media In many ways, social-media platforms are simply giant algorithms. At their heart, they work out what you're interested in and then give you more of it - using as many data points as they can get their hands on. Every "like", watch, click is stored. Most apps also glean more data from your web-browsing habits or geographical data. The idea is to predict the content you want and keep you scrolling - and it works. And those same algorithms that know you enjoy a cute-cat video are also deployed to sell you stuff. All the data social-media companies collect about you can also tailor ads to you in an incredibly accurate way. But these algorithms can go seriously wrong. They have been proved to push people towards hateful and extremist content. Extreme content simply does better than nuance on social media. And algorithms know that. Facebook's own civil-rights audit called for the company to do everything in its power to prevent its algorithm from "driving people toward self-reinforcing echo chambers of extremism". And last month we reported on how algorithms on online retail sites - designed to work out what you want to buy - were pushing racist and hateful products. Insurance Whether it's house, car, health or any other form of insurance, your insurer has to somehow assess the chances of something actually going wrong. In many ways, the insurance industry pioneered using data about the past to determine future outcomes - that's the basis of the whole sector, according to Timandra Harkness, author of Big Data: Does Size Matter. Getting a computer to do it was always going to be the logical next step. "Algorithms can affect your life very much and yet you as an individual don't necessarily get a lot of input," she says. "We all know if you move to a different postcode, your insurance goes up or down. "That's not because of you, it's because other people have been more or less likely to have been victims of crime, or had accidents or whatever." Innovations such as the "black box" that can be installed in a car to monitor how an individual drives have helped to lower the cost of car insurance for careful drivers who find themselves in a high-risk group. Might we see more personally tailored insurance quotes as algorithms learn more about our own circumstances? "Ultimately the point of insurance is to share the risk - so everybody puts [money] in and the people who need it take it out," Timandra says. "We live in an unfair world, so any model you make is going to be unfair in one way or another." Healthcare Artificial Intelligence is making great leaps in being able to diagnose various conditions and even suggest treatment paths. A study published in January 2020 suggested an algorithm performed better than human doctors when it came to identifying breast cancer from mammograms. And other successes include: However, all this requires a vast amount of patient data to train the programmes - and that is, frankly, a rather large can of worms. In 2017, the UK Information Commission ruled the Royal Free NHS Foundation Trust had not done enough to safeguard patient data when it had shared 1.6 million patient records with Google's AI division, DeepMind. "There's a fine line between finding exciting new ways to improve care and moving ahead of patients' expectations," said DeepMind's co-founder Mustafa Suleyman at the time. Policing Big data and machine learning have the potential to revolutionise policing. In theory, algorithms have the power to deliver on the sci-fi promise of "predictive policing" - using data, such as where crime has happened in the past, when and by whom, to predict where to allocate police resources. But that method can create algorithmic bias - and even algorithmic racism. "It's the same situation as you have with the exam grades," says Areeq Chowdhury, from technology think tank WebRoots Democracy. "Why are you judging one individual based on what other people have historically done? The same communities are always over-represented". Earlier this year, the defence and security think tank RUSI published a report into algorithmic policing. It raised concerns about the lack of national guidelines or impact assessments. It also called for more research into how these algorithms might exacerbate racism. Facial recognition too - used by police forces in the UK including the Met - has also been criticised. For example, there have been concerns about whether the data going into facial-recognition technology can make the algorithm racist. The charge is facial-recognition cameras are more accurate at identifying white faces - because they have more data on white faces. "The question is, are you testing it on a diverse enough demographic of people?" Areeq says. "What you don't want is a situation where some groups are being misidentified as a criminal because of the algorithm."

Here is an summary requirement:
Summarize the list of examples of algorithms making big decisions about our lives.

Please summarize the above article based on the given requirement:

# Output (a):
The article discusses how algorithms are being used to make big decisions in various aspects of our lives. An algorithm was used to to determine this year's A-level results based on previous schools' results, but this meant about 40% of this year's results came out lower than predicted. Social media platforms use algorithms to predict and provide content that users are interested in, but these algorithms can also push people towards extremist content. Insurance companies use algorithms to assess the chances of something going wrong and determine insurance quotes. Healthcare is also using algorithms to diagnose and suggest treatment paths, but this requires a vast amount of patient data to train the programs. Policing is another area where algorithms have the potential to revolutionize policing, but there are concerns about algorithmic bias and racism. Facial recognition technology has also been criticized for being more accurate at identifying white faces, leading to misidentification of certain groups as criminals.

# Output (b):
The article lists several examples of algorithms making big decisions about our lives, including social media, insurance, healthcare, and policing. In each case, the algorithm can have a significant impact on individuals, but there is often little transparency about how the algorithm works or how it is making decisions. This can lead to problems such as bias, racism, and unfairness.

# Which is better, Output (a) or Output (b)? Your response should be either "Output (a)" or "Output (b)":

Output (a)

Instruction:
Here is an article:
Thousands of students in England are angry about the controversial use of an algorithm to determine this year's GCSE and A-level results. By James Clayton and Zoe KleinmanTechnology reporters, BBC News They were unable to sit exams because of lockdown, so the algorithm used data about schools' results in previous years to determine grades. It meant about 40% of this year's A-level results came out lower than predicted, which has a huge impact on what students are able to do next. GCSE results are due out on Thursday. There are many examples of algorithms making big decisions about our lives, without us necessarily knowing how or when they do it. Here's a look at some of them. Social media In many ways, social-media platforms are simply giant algorithms. At their heart, they work out what you're interested in and then give you more of it - using as many data points as they can get their hands on. Every "like", watch, click is stored. Most apps also glean more data from your web-browsing habits or geographical data. The idea is to predict the content you want and keep you scrolling - and it works. And those same algorithms that know you enjoy a cute-cat video are also deployed to sell you stuff. All the data social-media companies collect about you can also tailor ads to you in an incredibly accurate way. But these algorithms can go seriously wrong. They have been proved to push people towards hateful and extremist content. Extreme content simply does better than nuance on social media. And algorithms know that. Facebook's own civil-rights audit called for the company to do everything in its power to prevent its algorithm from "driving people toward self-reinforcing echo chambers of extremism". And last month we reported on how algorithms on online retail sites - designed to work out what you want to buy - were pushing racist and hateful products. Insurance Whether it's house, car, health or any other form of insurance, your insurer has to somehow assess the chances of something actually going wrong. In many ways, the insurance industry pioneered using data about the past to determine future outcomes - that's the basis of the whole sector, according to Timandra Harkness, author of Big Data: Does Size Matter. Getting a computer to do it was always going to be the logical next step. "Algorithms can affect your life very much and yet you as an individual don't necessarily get a lot of input," she says. "We all know if you move to a different postcode, your insurance goes up or down. "That's not because of you, it's because other people have been more or less likely to have been victims of crime, or had accidents or whatever." Innovations such as the "black box" that can be installed in a car to monitor how an individual drives have helped to lower the cost of car insurance for careful drivers who find themselves in a high-risk group. Might we see more personally tailored insurance quotes as algorithms learn more about our own circumstances? "Ultimately the point of insurance is to share the risk - so everybody puts [money] in and the people who need it take it out," Timandra says. "We live in an unfair world, so any model you make is going to be unfair in one way or another." Healthcare Artificial Intelligence is making great leaps in being able to diagnose various conditions and even suggest treatment paths. A study published in January 2020 suggested an algorithm performed better than human doctors when it came to identifying breast cancer from mammograms. And other successes include: However, all this requires a vast amount of patient data to train the programmes - and that is, frankly, a rather large can of worms. In 2017, the UK Information Commission ruled the Royal Free NHS Foundation Trust had not done enough to safeguard patient data when it had shared 1.6 million patient records with Google's AI division, DeepMind. "There's a fine line between finding exciting new ways to improve care and moving ahead of patients' expectations," said DeepMind's co-founder Mustafa Suleyman at the time. Policing Big data and machine learning have the potential to revolutionise policing. In theory, algorithms have the power to deliver on the sci-fi promise of "predictive policing" - using data, such as where crime has happened in the past, when and by whom, to predict where to allocate police resources. But that method can create algorithmic bias - and even algorithmic racism. "It's the same situation as you have with the exam grades," says Areeq Chowdhury, from technology think tank WebRoots Democracy. "Why are you judging one individual based on what other people have historically done? The same communities are always over-represented". Earlier this year, the defence and security think tank RUSI published a report into algorithmic policing. It raised concerns about the lack of national guidelines or impact assessments. It also called for more research into how these algorithms might exacerbate racism. Facial recognition too - used by police forces in the UK including the Met - has also been criticised. For example, there have been concerns about whether the data going into facial-recognition technology can make the algorithm racist. The charge is facial-recognition cameras are more accurate at identifying white faces - because they have more data on white faces. "The question is, are you testing it on a diverse enough demographic of people?" Areeq says. "What you don't want is a situation where some groups are being misidentified as a criminal because of the algorithm."

Here is an summary requirement:
Summarize the list of examples of algorithms making big decisions about our lives.

Please summarize the above article based on the given requirement:

The article discusses how algorithms are being used to make big decisions in various aspects of our lives. An algorithm was used to to determine this year's A-level results based on previous schools' results, but this meant about 40% of this year's results came out lower than predicted. Social media platforms use algorithms to predict and provide content that users are interested in, but these algorithms can also push people towards extremist content. Insurance companies use algorithms to assess the chances of something going wrong and determine insurance quotes. Healthcare is also using algorithms to diagnose and suggest treatment paths, but this requires a vast amount of patient data to train the programs. Policing is another area where algorithms have the potential to revolutionize policing, but there are concerns about algorithmic bias and racism. Facial recognition technology has also been criticized for being more accurate at identifying white faces, leading to misidentification of certain groups as criminals.

The article lists several examples of algorithms making big decisions about our lives, including social media, insurance, healthcare, and policing. In each case, the algorithm can have a significant impact on individuals, but there is often little transparency about how the algorithm works or how it is making decisions. This can lead to problems such as bias, racism, and unfairness.


You are a helpful assistant in evaluating the quality of the outputs for a given instruction. Your goal is to select the best output for the given instruction.

Select the Output (a) or Output (b) that is better for the given instruction. The two outputs are generated by two different AI chatbots respectively.

Here are some rules of the evaluation:
(1) You should prioritize evaluating whether the output honestly/precisely/closely executes the instruction, then consider its helpfulness, accuracy, level of detail, harmlessness, etc.
(2) Outputs should NOT contain more/less than what the instruction asks for, as such outputs do NOT precisely execute the instruction.
(3) You should avoid any potential bias and your judgment should be as objective as possible. For example, the order in which the outputs were presented should NOT affect your judgment, as Output (a) and Output (b) are **equally likely** to be the better.

Do NOT provide any explanation for your choice.
Do NOT say both / neither are good.
You should answer using ONLY "Output (a)" or "Output (b)". Do NOT output any other words.

# Instruction:
Here is an article:
Thousands of students in England are angry about the controversial use of an algorithm to determine this year's GCSE and A-level results. By James Clayton and Zoe KleinmanTechnology reporters, BBC News They were unable to sit exams because of lockdown, so the algorithm used data about schools' results in previous years to determine grades. It meant about 40% of this year's A-level results came out lower than predicted, which has a huge impact on what students are able to do next. GCSE results are due out on Thursday. There are many examples of algorithms making big decisions about our lives, without us necessarily knowing how or when they do it. Here's a look at some of them. Social media In many ways, social-media platforms are simply giant algorithms. At their heart, they work out what you're interested in and then give you more of it - using as many data points as they can get their hands on. Every "like", watch, click is stored. Most apps also glean more data from your web-browsing habits or geographical data. The idea is to predict the content you want and keep you scrolling - and it works. And those same algorithms that know you enjoy a cute-cat video are also deployed to sell you stuff. All the data social-media companies collect about you can also tailor ads to you in an incredibly accurate way. But these algorithms can go seriously wrong. They have been proved to push people towards hateful and extremist content. Extreme content simply does better than nuance on social media. And algorithms know that. Facebook's own civil-rights audit called for the company to do everything in its power to prevent its algorithm from "driving people toward self-reinforcing echo chambers of extremism". And last month we reported on how algorithms on online retail sites - designed to work out what you want to buy - were pushing racist and hateful products. Insurance Whether it's house, car, health or any other form of insurance, your insurer has to somehow assess the chances of something actually going wrong. In many ways, the insurance industry pioneered using data about the past to determine future outcomes - that's the basis of the whole sector, according to Timandra Harkness, author of Big Data: Does Size Matter. Getting a computer to do it was always going to be the logical next step. "Algorithms can affect your life very much and yet you as an individual don't necessarily get a lot of input," she says. "We all know if you move to a different postcode, your insurance goes up or down. "That's not because of you, it's because other people have been more or less likely to have been victims of crime, or had accidents or whatever." Innovations such as the "black box" that can be installed in a car to monitor how an individual drives have helped to lower the cost of car insurance for careful drivers who find themselves in a high-risk group. Might we see more personally tailored insurance quotes as algorithms learn more about our own circumstances? "Ultimately the point of insurance is to share the risk - so everybody puts [money] in and the people who need it take it out," Timandra says. "We live in an unfair world, so any model you make is going to be unfair in one way or another." Healthcare Artificial Intelligence is making great leaps in being able to diagnose various conditions and even suggest treatment paths. A study published in January 2020 suggested an algorithm performed better than human doctors when it came to identifying breast cancer from mammograms. And other successes include: However, all this requires a vast amount of patient data to train the programmes - and that is, frankly, a rather large can of worms. In 2017, the UK Information Commission ruled the Royal Free NHS Foundation Trust had not done enough to safeguard patient data when it had shared 1.6 million patient records with Google's AI division, DeepMind. "There's a fine line between finding exciting new ways to improve care and moving ahead of patients' expectations," said DeepMind's co-founder Mustafa Suleyman at the time. Policing Big data and machine learning have the potential to revolutionise policing. In theory, algorithms have the power to deliver on the sci-fi promise of "predictive policing" - using data, such as where crime has happened in the past, when and by whom, to predict where to allocate police resources. But that method can create algorithmic bias - and even algorithmic racism. "It's the same situation as you have with the exam grades," says Areeq Chowdhury, from technology think tank WebRoots Democracy. "Why are you judging one individual based on what other people have historically done? The same communities are always over-represented". Earlier this year, the defence and security think tank RUSI published a report into algorithmic policing. It raised concerns about the lack of national guidelines or impact assessments. It also called for more research into how these algorithms might exacerbate racism. Facial recognition too - used by police forces in the UK including the Met - has also been criticised. For example, there have been concerns about whether the data going into facial-recognition technology can make the algorithm racist. The charge is facial-recognition cameras are more accurate at identifying white faces - because they have more data on white faces. "The question is, are you testing it on a diverse enough demographic of people?" Areeq says. "What you don't want is a situation where some groups are being misidentified as a criminal because of the algorithm."

Here is an summary requirement:
Summarize the list of examples of algorithms making big decisions about our lives.

Please summarize the above article based on the given requirement:

# Output (a):
The article discusses how algorithms are being used to make big decisions in various aspects of our lives. Social media platforms use algorithms to predict and provide content that users are interested in, but these algorithms can also push people towards extremist content. Insurance companies use algorithms to assess the chances of something going wrong and determine insurance quotes. Healthcare is also using algorithms to diagnose and suggest treatment paths, but this requires a vast amount of patient data to train the programs. Policing is another area where algorithms have the potential to revolutionize policing, but there are concerns about algorithmic bias and racism. Facial recognition technology has also been criticized for being more accurate at identifying white faces, leading to misidentification of certain groups as criminals.

# Output (b):
Algorithms are being used to make big decisions about our lives in many ways, such as on social media platforms, in the insurance industry, in healthcare, and in policing. Social media algorithms work out what users are interested in and give them more of it, while insurance algorithms use data about the past to determine future outcomes. AI is being used to diagnose conditions and suggest treatment paths in healthcare, while predictive policing uses data to allocate police resources. Facial recognition technology has also been criticized for potentially exacerbating racism.

# Which is better, Output (a) or Output (b)? Your response should be either "Output (a)" or "Output (b)":

Output (a)

Instruction:
Here is an article:
Thousands of students in England are angry about the controversial use of an algorithm to determine this year's GCSE and A-level results. By James Clayton and Zoe KleinmanTechnology reporters, BBC News They were unable to sit exams because of lockdown, so the algorithm used data about schools' results in previous years to determine grades. It meant about 40% of this year's A-level results came out lower than predicted, which has a huge impact on what students are able to do next. GCSE results are due out on Thursday. There are many examples of algorithms making big decisions about our lives, without us necessarily knowing how or when they do it. Here's a look at some of them. Social media In many ways, social-media platforms are simply giant algorithms. At their heart, they work out what you're interested in and then give you more of it - using as many data points as they can get their hands on. Every "like", watch, click is stored. Most apps also glean more data from your web-browsing habits or geographical data. The idea is to predict the content you want and keep you scrolling - and it works. And those same algorithms that know you enjoy a cute-cat video are also deployed to sell you stuff. All the data social-media companies collect about you can also tailor ads to you in an incredibly accurate way. But these algorithms can go seriously wrong. They have been proved to push people towards hateful and extremist content. Extreme content simply does better than nuance on social media. And algorithms know that. Facebook's own civil-rights audit called for the company to do everything in its power to prevent its algorithm from "driving people toward self-reinforcing echo chambers of extremism". And last month we reported on how algorithms on online retail sites - designed to work out what you want to buy - were pushing racist and hateful products. Insurance Whether it's house, car, health or any other form of insurance, your insurer has to somehow assess the chances of something actually going wrong. In many ways, the insurance industry pioneered using data about the past to determine future outcomes - that's the basis of the whole sector, according to Timandra Harkness, author of Big Data: Does Size Matter. Getting a computer to do it was always going to be the logical next step. "Algorithms can affect your life very much and yet you as an individual don't necessarily get a lot of input," she says. "We all know if you move to a different postcode, your insurance goes up or down. "That's not because of you, it's because other people have been more or less likely to have been victims of crime, or had accidents or whatever." Innovations such as the "black box" that can be installed in a car to monitor how an individual drives have helped to lower the cost of car insurance for careful drivers who find themselves in a high-risk group. Might we see more personally tailored insurance quotes as algorithms learn more about our own circumstances? "Ultimately the point of insurance is to share the risk - so everybody puts [money] in and the people who need it take it out," Timandra says. "We live in an unfair world, so any model you make is going to be unfair in one way or another." Healthcare Artificial Intelligence is making great leaps in being able to diagnose various conditions and even suggest treatment paths. A study published in January 2020 suggested an algorithm performed better than human doctors when it came to identifying breast cancer from mammograms. And other successes include: However, all this requires a vast amount of patient data to train the programmes - and that is, frankly, a rather large can of worms. In 2017, the UK Information Commission ruled the Royal Free NHS Foundation Trust had not done enough to safeguard patient data when it had shared 1.6 million patient records with Google's AI division, DeepMind. "There's a fine line between finding exciting new ways to improve care and moving ahead of patients' expectations," said DeepMind's co-founder Mustafa Suleyman at the time. Policing Big data and machine learning have the potential to revolutionise policing. In theory, algorithms have the power to deliver on the sci-fi promise of "predictive policing" - using data, such as where crime has happened in the past, when and by whom, to predict where to allocate police resources. But that method can create algorithmic bias - and even algorithmic racism. "It's the same situation as you have with the exam grades," says Areeq Chowdhury, from technology think tank WebRoots Democracy. "Why are you judging one individual based on what other people have historically done? The same communities are always over-represented". Earlier this year, the defence and security think tank RUSI published a report into algorithmic policing. It raised concerns about the lack of national guidelines or impact assessments. It also called for more research into how these algorithms might exacerbate racism. Facial recognition too - used by police forces in the UK including the Met - has also been criticised. For example, there have been concerns about whether the data going into facial-recognition technology can make the algorithm racist. The charge is facial-recognition cameras are more accurate at identifying white faces - because they have more data on white faces. "The question is, are you testing it on a diverse enough demographic of people?" Areeq says. "What you don't want is a situation where some groups are being misidentified as a criminal because of the algorithm."

Here is an summary requirement:
Summarize the list of examples of algorithms making big decisions about our lives.

Please summarize the above article based on the given requirement:

The article discusses how algorithms are being used to make big decisions in various aspects of our lives. Social media platforms use algorithms to predict and provide content that users are interested in, but these algorithms can also push people towards extremist content. Insurance companies use algorithms to assess the chances of something going wrong and determine insurance quotes. Healthcare is also using algorithms to diagnose and suggest treatment paths, but this requires a vast amount of patient data to train the programs. Policing is another area where algorithms have the potential to revolutionize policing, but there are concerns about algorithmic bias and racism. Facial recognition technology has also been criticized for being more accurate at identifying white faces, leading to misidentification of certain groups as criminals.

Algorithms are being used to make big decisions about our lives in many ways, such as on social media platforms, in the insurance industry, in healthcare, and in policing. Social media algorithms work out what users are interested in and give them more of it, while insurance algorithms use data about the past to determine future outcomes. AI is being used to diagnose conditions and suggest treatment paths in healthcare, while predictive policing uses data to allocate police resources. Facial recognition technology has also been criticized for potentially exacerbating racism.