It is based on the article ‘Dealing with algorithmic bias in news‘, which we recommend trainers read before adapting this outline for your own purposes.

Learning objective

Students will identify and analyse instances of algorithmic bias in news content. They will evaluate the impact of such biases on news quality and credibility, using real-world case studies.

• Student-facing objective: By the end of this lesson the student will be able to spot and assess algorithmic bias in news stories and understand how it affects the news we read and produce.
• Standards: Journalist will learn how to spot and deal with algorithmic bias in news

Learning activities

Warm-up

Display a short news headline generated by an algorithm. Ask students: “What do you notice? What do you wonder?” Give them a few minutes to think and discuss with a partner. Then, select a few students to share their observations and questions. Record these for all to see. Guide the conversation towards recognising potential biases in the headline, such as language choice or topic emphasis. This primes students for deeper exploration of algorithm bias in news.

Direct instruction

• Introduction to algorithmic bias: Explain the concept of algorithmic bias. Define key terms: selection bias, confirmation bias, and representation bias. Use a real example, such as a news recommendation engine that consistently suggests similar types of stories, to illustrate how bias can manifest. Discuss the impact of these biases on news quality and credibility.
• Sources of algorithmic bias: Describe the origins of algorithmic bias. Highlight three main sources: biased data, biased algorithms, and lack of transparency. Provide a case study, such as the Facebook Cambridge Analytica scandal, to demonstrate how biased data can lead to skewed news coverage. Encourage students to consider how transparency in algorithm design could mitigate these issues.
• Case studies and lessons learned: Present a case study where algorithm bias affected news coverage, such as the YouTube algorithm’s role in spreading misinformation. Discuss the lessons learned from this case. Ask students to identify signs of bias and suggest strategies for reducing its impact. Emphasise the importance of human oversight and editorial judgment in balancing algorithmic recommendations with journalistic integrity.

Guided practice

Think, Pair, Share: Guide students through a structured discussion to identify algorithmic bias in news content.

• Think: Present a news article generated by an algorithm. Ask students to individually identify potential biases, focusing on language, source selection, and representation.
• Pair: Have students pair up to discuss their findings. Encourage them to compare observations and refine their understanding of algorithmic bias.
• Share: Facilitate a class discussion where pairs share their insights. Record key points on the board, highlighting common biases and unique observations.
• Connect: Relate the discussion back to the case studies covered in direct instruction. Ask students to connect their findings with real-world examples.
• Reflect: Conclude with a reflection on how recognising algorithmic bias can improve news quality and credibility. Encourage students to consider the role of journalists in mitigating bias.

Independent practice

• Bias detection exercise: Provide students with a set of news articles generated by algorithms. Instruct them to identify and annotate instances of potential bias, focusing on language, source selection, and representation. Use a digital tool or printed copies for annotations.
• Analysis and reflection: Ask students to write a brief analysis of their findings, discussing the impact of identified biases on news quality and credibility. Encourage them to propose strategies for mitigating these biases.
• Peer review: Pair students to exchange their analyses. Have them provide constructive feedback on each other’s work, focusing on the clarity of bias identification and the feasibility of proposed strategies.

Assignment

Ask students:

1. What is one example of algorithmic bias you identified in today’s lesson?
2. How can journalists help mitigate algorithmic bias in news content?
3. What’s one question you still have from today’s lesson?

Suggested answers:

• Suggested answer to Question 1: An example is the YouTube algorithm promoting misinformation by favouring sensational content.
• Suggested answer to Question 2: Journalists can advocate for transparency in algorithm design and apply human oversight to ensure balanced news coverage.

Teacher resources

Differentiation guide

• Advanced learners: Encourage deeper analysis by having them explore additional case studies or research articles on algorithmic bias. Challenge them to propose innovative solutions for mitigating bias, considering ethical implications and technological advancements.
• Striving learners: Simplify concepts by using more relatable examples and providing additional scaffolding, such as graphic organisers or guided notes. Offer more structured support during activities, such as pairing them with peers who can assist in discussions and exercises.
• Recommended reading: This lesson plan is based on the article ‘Dealing with algorithmic bias in news‘, which we recommend trainers read before adapting this outline for your own purposes.

Notable definitions

Algorithmic bias: The systematic and repeatable errors in a computer system that create unfair outcomes, such as privileging one arbitrary group of users over others.

Selection bias: A type of bias caused by selecting non-random data for analysis, which can lead to inaccurate conclusions.

Confirmation bias: The tendency to favour information that confirms existing beliefs or hypotheses, often leading to skewed data interpretation.

Required materials

• Computers: For accessing digital news articles and annotation tools.
• Printed copies: News articles for annotation exercises (optional).
• Digital annotation tools: Software or apps for marking up digital articles.
• Whiteboard/Markers: For recording observations and discussion points.
• Projector: To display news headlines and case studies.
• Notebooks/Pens: For students to take notes and write reflections.

Lesson summary

• Warm-up
• Direct instruction
• Guided practice
• Independent practice
• Assignment

The free teaching tools at the Khan Academy were used in the production of this lesson plan.

Related article

Dealing with algorithmic bias in news

The post Lesson: Algorithmic bias first appeared on Media Helping Media.

Journalists need to be trained in how to recognise and deal with algorithmic bias in order to counter the speed and scale at which biased content spreads.

That’s the view of Gemini, the artificial intelligence (AI) conversation tool, which says that the need for such training is “more urgent than ever”.

According to AI:

“the rapid advancement of artificial intelligence and machine learning has led to a significant increase in the use of algorithms in news production…

… algorithms can introduce bias into news content, which can have serious consequences for both individuals and society.”

Media Helping Media (MHM) asked Gemini how journalists can recognise and deal with algorithmic bias.

We also asked it to design a one-day training course to equip journalists with the tools they need to tackle the issue.

The following is the text of that conversation, which took place in September 2024. During the conversation Gemini states that while the challenge of bias in news production is not entirely new, the rise of AI has made it more urgent and complex.

AI warns that:

“journalists and news organisations must adapt to this new reality by developing strategies to mitigate algorithm bias and ensure that the news they produce is fair, accurate, and representative”.

We then asked AI the following questions. The answers were immediate. Gemini’s responses are unedited other than to change them from American English to English English  (MHM’s preferred house style).

MHM: What is algorithmic bias in news production?

Gemini AI: Algorithmic bias in news production refers to the unintentional or intentional bias introduced into news content by the algorithms used to curate, personalise, or generate news. This bias can manifest in various ways, including:

• Selection bias: Algorithms may prioritise certain stories or topics over others, leading to a skewed representation of the news.
• Confirmation bias: Algorithms may reinforce existing beliefs or biases by recommending content that aligns with a user’s preferences.
• Representation bias: Algorithms may underrepresent certain groups or perspectives, leading to a lack of diversity in the news.
• Amplification bias: Algorithms may amplify certain narratives or misinformation, contributing to the spread of false or misleading information.

Factors contributing to algorithmic bias:

• Biased data: If the data used to train algorithms is biased, the algorithms will likely produce biased outputs.
• Biased algorithms: The design of algorithms themselves can introduce bias, such as by prioritising certain features or metrics over others.
• Lack of transparency: The lack of transparency in how algorithms work can make it difficult to identify and address bias.

Addressing algorithmic bias in news production requires a multi-faceted approach, including:

• Improving data quality: Ensuring that the data used to train algorithms is diverse, representative, and free from bias.
• Designing unbiased algorithms: Developing algorithms that are transparent, accountable, and resistant to bias.
• Promoting transparency: Increasing transparency in how algorithms are used and the decisions they make.
• Human oversight: Ensuring that human editors and journalists play a role in reviewing and curating news content.

By understanding and addressing algorithmic bias, news organisations can help to ensure that the news they produce is fair, accurate, and representative.

MHM: How can journalists avoid algorithmic bias if they are not in control of the algorithms?

Gemini AI: While journalists may not have direct control over the algorithms used in news production, they can take several steps to mitigate algorithmic bias:

1. Be aware of algorithmic bias: Journalists should understand the concept of algorithmic bias and how it can manifest in news content. This knowledge can help them identify potential biases and take steps to address them.
2. Critically evaluate news sources: Journalists should be sceptical of news stories generated or curated by algorithms. They should carefully evaluate the sources of information and verify the accuracy of the content.
3. Seek diverse sources: Journalists should strive to gather information from a variety of sources, including those that may not be prominently featured in algorithm-driven news feeds. This can help to ensure that the news they report is balanced and representative.
4. Use human judgement: Journalists should rely on their own judgement to assess the quality and relevance of news stories. They should be willing to override algorithmic recommendations if they believe that they are biased or misleading.
5. Advocate for transparency: Journalists can advocate for greater transparency in how algorithms are used in news production. This can help to identify and address biases and ensure that the news is produced in a fair and ethical manner.
6. Collaborate with data scientists: Journalists can collaborate with data scientists to understand how algorithms work and identify potential biases. This can help them to develop strategies for mitigating algorithm bias.

By taking these steps, journalists can help to ensure that the news they report is accurate, balanced, and free from algorithmic bias.

MHM: So mitigating algorithmic bias is a new task brought about by the advance of AI but should now become a crucial part of a news producer’s job?

Gemini AI: That’s a great observation. While the term ‘algorithmic bias’ may be relatively new, the underlying issue of bias in news production has existed for centuries. However, the advent of AI and machine learning has amplified the potential for bias to be introduced into news content at a larger scale and at a faster pace.

Here’s a breakdown of how traditional news production compared to AI-driven news:

Traditional news production:

• Human curation: News editors and journalists manually select and curate stories based on their judgement and expertise.
• Limited reach: News distribution was primarily through print and broadcast media, limiting the audience and potential for bias.

AI-driven news production:

• Algorithmic curation: Algorithms use data to recommend news stories to users, often based on their past behaviour and preferences.
• Vast reach: News can be disseminated instantly to a global audience through digital platforms, increasing the potential impact of bias.

Key differences:

• Scale: AI can process and analyse vast amounts of data, increasing the potential for bias to be introduced at a larger scale.
• Speed: AI can disseminate news at a much faster pace than traditional methods, making it difficult to identify and correct biases before they have a significant impact.
• Personalisation: AI can personalise news content to individual users, potentially reinforcing existing biases and limiting exposure to diverse perspectives.

Therefore, while the challenge of bias in news production is not entirely new, the rise of AI has made it more urgent and complex. Journalists and news organisations must adapt to this new reality by developing strategies to mitigate algorithmic bias and ensure that the news they produce is fair, accurate, and representative.

MHM: So would you say that dealing with algorithmic bias should now be part of every journalist’s basic training?

Gemini AI: Absolutely. Dealing with algorithmic bias should be a fundamental part of every journalist’s training in the digital age. Here’s why:

1. Understanding the problem: Journalists need to understand how algorithms work, how they can introduce bias, and the potential consequences of this bias.
2. Identifying and mitigating bias: Journalists should be able to identify signs of algorithmic bias in news content and take steps to mitigate its effects.
3. Evaluating sources: Journalists should be able to critically evaluate the sources of information they rely on, especially those that are generated or curated by algorithms.
4. Promoting diversity and inclusion: Journalists should strive to promote diversity and inclusion in the news they report, by seeking out a variety of sources and avoiding algorithmic biases that may underrepresent certain groups or perspectives.
5. Advocating for transparency: Journalists can advocate for greater transparency in how algorithms are used in news production, which can help to identify and address biases.

By incorporating training on algorithmic bias into journalism programmes, we can equip journalists with the skills and knowledge they need to produce fair, accurate, and representative news in the digital age.

MHM: Would you say that the need for journalists to be trained in dealing with algorithmic bias is urgent?

Gemini AI: Absolutely. The need for journalists to be trained in dealing with algorithmic bias is more urgent than ever. The rapid advancement of artificial intelligence and machine learning has led to a significant increase in the use of algorithms in news production. These algorithms can introduce bias into news content, which can have serious consequences for both individuals and society.

Here are some reasons why this training is urgent:

• Increasing reliance on algorithms: News organisations are increasingly relying on algorithms to curate, personalise, and generate news content. This means that the potential for algorithmic bias to impact news coverage is growing.
• The potential for harm: Algorithmic bias can lead to the spread of misinformation, the amplification of harmful stereotypes, and the underrepresentation of certain groups or perspectives. This can have serious consequences for individuals and society.
• The need for accountability: Journalists have a responsibility to ensure that the news they report is accurate, fair, and unbiased. This includes understanding and addressing the potential for algorithmic bias.

By training journalists in how to identify and mitigate algorithmic bias, we can help to ensure that the news we consume is reliable and trustworthy.

MHM’s conclusion

The fact that AI is warning that journalists need to be trained in recognising and dealing with algorithmic bias suggests that human judgement, journalistic ethics and editorial values are crucial in a world where artificial intelligence and machine learning is crafting so much content.

As Gemini says in the interview above, “the underlying issue of bias in news production has existed for centuries. However, the advent of AI and machine learning has amplified the potential for bias to be introduced into news content at a larger scale and at a faster pace.”

Questions

1. What is algorithmic bias in news production, and how can it manifest in news content?
2. Identify and describe two factors that contribute to algorithmic bias in news production.
3. Explain why the rise of AI has made the challenge of bias in news production more urgent and complex.
4. What are some steps journalists can take to mitigate algorithmic bias, even if they do not control the algorithms?
5. Discuss the role of human oversight in addressing algorithmic bias in news production.
6. How does algorithmic curation differ from traditional human curation in news production?
7. Why is it important for journalists to seek diverse sources when reporting news?
8. Evaluate the potential consequences of algorithmic bias on society and individuals.
9. How can journalists advocate for greater transparency in the use of algorithms in news production?
10. In what ways can collaboration between journalists and data scientists help address algorithm bias?

Answers

1. Algorithmic bias in news production refers to the unintentional or intentional bias introduced into news content by algorithms. It can manifest as selection bias, confirmation bias, representation bias, or amplification bias.
2. Two factors contributing to algorithmic bias are biased data, where the data used to train algorithms is biased, and biased algorithms, where the design of algorithms prioritises certain features or metrics over others.
3. The rise of AI has made bias in news production more urgent and complex due to the increased scale and speed at which AI can process and disseminate news, as well as the potential for personalised content to reinforce existing biases.
4. Journalists can mitigate algorithmic bias by being aware of it, critically evaluating news sources, seeking diverse sources, using human judgment, advocating for transparency, and collaborating with data scientists.
5. Human oversight involves journalists and editors reviewing and curating news content to ensure it is fair, accurate, and representative, thus helping to identify and correct algorithmic biases.
6. Algorithmic curation uses data to recommend news stories based on user behaviour and preferences, while traditional human curation relies on the judgment and expertise of news editors and journalists.
7. Seeking diverse sources is important to ensure balanced and representative news coverage, avoiding the reinforcement of biases that may underrepresent certain groups or perspectives.
8. Algorithmic bias can lead to the spread of misinformation, amplification of harmful stereotypes, and underrepresentation of certain groups, which can have serious consequences for individuals and society.
9. Journalists can advocate for transparency by pushing for clear explanations of how algorithms are used in news production and the criteria they use for decision-making.
10. Collaboration with data scientists helps journalists understand how algorithms work and identify potential biases, allowing them to develop strategies to mitigate algorithmic bias.

Lesson plan for trainers

If you are a trainer of journalists we have a free lesson plan: ‘Algorithmic bias’ which you are welcome to download and adapt for your own purposes.

The post Dealing with algorithmic bias in news first appeared on Media Helping Media.

Discover how to create concise, compelling TV and radio packages that capture audience attention and deliver key information effectively.

This training module was written for journalism students in Jaffna, Sri Lanka. They were studying broadcast journalism, and in particular creating news features for radio. Many of those attending the course had no previous journalism experience or training.

TV and radio packages

Structure, timing, and letting the interview breathe are all essential elements for ensuring a general TV or radio news package works.

These are the packages where you introduce the audience to an issue and explore multiple elements of the story through interviewing different people.

It’s also important not to cram too much into an item, perhaps just three points.

And try to avoid noddies (shots where you, the interviewer, nod and which are edited in later) and walking shots for TV, they are overused and boring.

Try to think of original shots and sounds that will capture the attention of the audience.

1. Clarity: Before you start, have a clear idea of how long your finished item is likely to be and roughly how much footage of your interviewees you are likely to use.
2. Format: Map out a structure for the piece and try to work out a likely order for the interview clips and which points they will address.
3. Main points: Try to limit yourself to three main points for one item.
4. Interviewees: Make sure each of these three points is addressed by a different interviewee.
5. Review: Listen or watch the interview in full from start to finish at least once in order to ensure you haven’t missed anything. Take notes of the time on the recording of each potential interview clip, the words that begin the clip and the words that end it.
6. Coherence: When you have repeated the process for all the interviews in your piece, return to the structure you have mapped out and see if it is still coherent or if the order of interviewees needs to change.
7. Strength: Try to put the strongest interview near the start of the piece.
8. Selection: When you are selecting interview clips, choose ones which give opinion over ones which relay only information; information which is not controversial can easily be summarised by you in your linking commentary.
9. Pace: Try to leave a pause at the start and end of each clip. Life isn’t breathless; neither should a radio or television package be.
10. Clichés: In television avoid using noddies and walking shots to illustrate your material. They are the mind-numbingly boring to look at and do not make best use of the medium.
11. Editing: Avoid cutting excessively from the answer (such as taking one part of three seconds from the start of an answer, three seconds from the middle, and five from the end). This sounds and looks unnatural, misrepresents the interviewee, and is excessively difficult to process for television interviews.
12. Context: Never take an answer from one question and use it in response to another. This is gross misrepresentation.
13. Commentary: When you are writing commentary to link the clips together, try to avoid using the same words at the end of your text as the interviewee says in the beginning of the clip ie. : John Smith said he was delighted.. [John Smith] “I am delighted …”
14. Summaries: In TV and radio journalism, your package may often be mentioned in a news bulletin in the form of a clip before its broadcast slot. When writing the introduction for that clip, avoid summarising everything that is going to feature in the clip.
15. Positioning: In television reports, try to stick to the convention of alternate interviewees being on opposite sides of the screen. (First interviewee looking left to right, second right to left, third left to right etc.)
16. Voices: Try to avoid running two clips back to back without a commentary in between. Where this is unavoidable, for example in the case of vox pops, try to alternate between male and female voices. The reason for this is to avoid confusion.
17. Titles: Always make sure that you have the correct title for your interviewee and the correct spelling of their name. This is particularly important for TV captions. If they have a particularly long job title, agree a shortened version before you return from the interview.
18. Ending: Try to avoid ending a report with a clip of one of the interviewees. In TV this looks untidy. In radio, it complicates life for the studio presenter. It also gives one side or another of an argument the last word.
19. Answers: If you are editing an interview as a stand-alone item, try to put as much of the non-controversial information in the intro or lead-in to the item, and always make sure the intro ends with a question and the piece begins with an answer to that question.
20. Options: For stand-alone interviews, always give an option of an early ending, with a shorter duration and the right out-words. This will help the production team in case more urgent news breaks or they need to cut back your item.

Questions

1. What are the essential elements for creating effective TV or radio news packages?
2. Why is it important to limit the number of main points in a TV or radio package?
3. How should interview clips be selected to enhance the quality of a news package?
4. What is the significance of leaving pauses at the start and end of each interview clip?
5. How can the coherence of a news package be ensured during the editing process?
6. Why should noddies and walking shots be avoided in television packages?
7. What are the potential consequences of excessive editing in interview clips?
8. How should commentary be crafted to effectively link interview clips together?
9. What is the recommended positioning for interviewees in television reports, and why is it important?
10. How can a stand-alone interview be structured to accommodate potential changes in broadcast timing?

Answers

1. Structure, timing, and allowing the interview to breathe are essential elements.
2. Limiting main points helps maintain clarity and focus, preventing information overload.
3. Select clips that provide opinions rather than just information to engage the audience.
4. Pauses create a natural flow and prevent the package from feeling rushed.
5. Review the structure and order of interviewees to ensure logical progression and coherence.
6. They are overused, boring, and do not make the best use of the medium.
7. Excessive editing can misrepresent the interviewee and make the package difficult to process.
8. Avoid repeating words from the end of the commentary and the start of the clip to maintain flow.
9. Alternate interviewees on opposite sides of the screen to avoid visual monotony.
10. Include an option for an early ending with appropriate out-words for flexibility in broadcast timing.

The post How to create a broadcast news package first appeared on Media Helping Media.

The news producer has an essential role to play in any news organisation. Their job is to ensure that the material that is published is of the highest quality.

News producers add depth to the content being produced, make sure it is well-researched, arrange interviews, manage resources, assist reporters and correspondents with the logistics involved in gathering and processing news, and oversee quality control.

Some media organisations, particularly newspapers, will have different names for the role, but whatever it is called, the person will be expected to ensure that all significant angles are followed up, and that related issues are investigated. Their job is to take the overview.

In many cases they are the bridge between the editors and reporters, ensuring that both can do their jobs efficiently and effectively.

Some media organisations try to cope without news producers. They expect the journalists and correspondents to take care of all their production needs.

Of course the stories will still get produced, but the likelihood is that they will not be as thorough, that effort and resources might be wasted, and sometimes deadlines won’t be met.

At times the news producers will be offering support from the newsroom, at other times they will be in the field. When they are working in the field, the relationship with the reporter or correspondent will probably be one-to-one.

However, when they are working from the newsroom, a good news producer might be able to help several journalists at the same time. And this is where the efficiencies start to show.

The role of the news producer

In an earlier module we looked at the best way to run a news meeting. The graphic, below, is taken from that training module as a reminder of the important role the news producer plays in the news business.

A journalist should be able to gather most of the information they need in order to produce a story. However, they are often out of the office where access to computers can be a problem.

They might be working to tight deadlines, and unable to research adequately. Perhaps they need to contact someone at short notice but don’t have the time to make the calls. It could be that they need to access archive material to add to their piece but they don’t have time to search for it or are unable to get back to the office in time.

They might need a comment from a correspondent for the piece but are unable to contact them. At times they might need to refer up the editorial chain regarding a legal or ethical matter but a senior editor is not available.

These are the times when a news producer, sitting in the office or working in the field, is invaluable. They can take on all these responsibilities. The graphic below shows how the process should work.

Qualities of a news producer

In some media organisations a news producer is a specialist job requiring someone with a keen eye for a news story, who has great research skills, who can demonstrate excellent organisational abilities, and who is able to see the bigger picture.

In some newsrooms the news producer is a career-progression role between the reporter and the editors. If they have worked as a reporter, both in the newsroom and in the field, they will be able to understand the pressures the journalist they are facing is under and will be able to provide the support they need.

Efficiencies and savings

For media managers, having news producers can result in efficiencies and savings.

They can help avoid duplication of effort, they can help manage scarce resources, they can help schedule editing to remove log jams and better utilise downtime, and they can act as a reference point where both senior and junior staff can turn in order to assess the status of a story and when it is expected to be ready.

The post The role of the news producer first appeared on Media Helping Media.