Tuesday January 21, 2020
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Here’s Why LinkedIn Relies on Users, Not AI, for Removing Fake Profiles

It, however, appears that LinkedIn relies more on users than its AI and ML solutions to keep its platform sanitised

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Just as in Twitter, on the LinkedIn mobile app, members can find the day's top stories on tapping inside the search bar. Pixabay

In February, an Indian Administrative Service (IAS) officer B. Chandrakala found a LinkedIn fake account running in her name. After registering a case under the Information Technology (IT) Act, the police swung into action and get LinkedIn to shut that face account.

Under investigation by the Central Bureau of Investigation (CBI) in an illegal mining case in Uttar Pradesh, Chandrakala was shocked to see the fake account being run on LinkedIn in her name using her photograph and designation and publishing objectionable obscene content.

Not just fake accounts, there have been several cases of fraudsters impersonating staffing agencies on the LinkedIn platform and people keeping duplicate and fake profiles.

The goal of such people, according to Bruce Johnston, a famed LinkedIn sales and marketing consultant, is to harvest email addresses from connections, identity theft, phishing, spear phishing and other scams and impersonation.

LinkedIn, which has over 54 million users in India which is its fastest growing market outside of the US, claims it is good at stamping out fake profiles once they are identified.

But the real game is to identify such problems firsthand — via Artificial Intelligence (AI)-enabled algorithms which the company has invested heavily in — in order to weed out bad actors quickly and act proactively, without waiting for users to flag such content.

Human-centric AI and Machine Learning (ML) is helping — to a great extent — Facebook, Twitter and Google stamp out bad content, terror-related posts, political interference, misinformation, abuse and several other inauthentic behaviours even before users flag them.

“LinkedIn is pretty good at stamping out fake profiles once they are identified. But as fake profiles can be replaced just as quickly as they are detected and stamped out, this is a real problem,” wrote Johnston in a blog post some time back.

India has witnessed nearly 80 per cent growth in Human Resource (HR) analytics professionals in the past five years, global professional network site LinkedIn said on Tuesday.
LinkedIn reports that HR professional number grew by 80% in last 5 years in India. Pixabay

LinkedIn does not have a satisfactory answer when it comes to identifying a person who is between jobs or joined at some other place but keeps his old profile on LinkedIn.

“Members come to LinkedIn to connect with their community, learn from each other and access opportunity. The best way to do that is to keep their profile updated, including sharing news and insights,” says the Microsoft-owned platform.

LinkedIn gives users option to flag inappropriate or fake profiles on its platform – profiles that contain profanity, empty profiles with fake names, or profiles that are impersonating public figures.

The company told IANS that while there may be multiple reasons why members take more time to update their profiles, it is possible for other members to report inaccurate information.

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“We take each report very seriously and our team reviews each case individually. If the information is inaccurate, we take action, which can include removing the content,” said a LinkedIn spokesperson.

Specifically for fake accounts, said LinkedIn, we investigate suspected violations of our Terms of Service, including the creation of false profiles, and take immediate action when violations are uncovered.

“If members use multiple email addresses to log into LinkedIn, this can lead to duplicate accounts. LinkedIn has tools in place to check for such instances and notify members to merge the duplicate accounts,” informed the company.

It, however, appears that LinkedIn relies more on users than its AI and ML solutions to keep its platform sanitised. (IANS)

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AI-based Google Model Beats Humans in Detecting Breast Cancer

This work, said Google, is the latest strand of its research looking into detection and diagnosis of breast cancer, not just within the scope of radiology, but also pathology

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The Google name is displayed outside the company's office in London, Britain. VOA

In a ray of hope for those who have to go for breast cancer screening and even for healthy women who get false alarms during digital mammography, an Artificial Intelligence (AI)-based Google model has left radiologists behind in spotting breast cancer by just scanning the X-ray results.

Reading mammograms is a difficult task, even for experts, and can often result in both false positives and false negatives.

In turn, these inaccuracies can lead to delays in detection and treatment, unnecessary stress for patients and a higher workload for radiologists who are already in short supply, Google said in a blog post on Wednesday.

Google’s AI model spotted breast cancer in de-identified screening mammograms (where identifiable information has been removed) with greater accuracy, fewer false positives and fewer false negatives than experts.

“This sets the stage for future applications where the model could potentially support radiologists performing breast cancer screenings,” said Shravya Shetty, Technical Lead, Google Health.

Digital mammography or X-ray imaging of the breast, is the most common method to screen for breast cancer, with over 42 million exams performed each year in the US and the UK combined.

“But despite the wide usage of digital mammography, spotting and diagnosing breast cancer early remains a challenge,” said Daniel Tse, Product Manager, Google Health.

Together with colleagues at DeepMind, Cancer Research UK Imperial Centre, Northwestern University and Royal Surrey County Hospital, Google set out to see if AI could support radiologists to spot the signs of breast cancer more accurately.

The findings, published in the journal Nature, showed that AI could improve the detection of breast cancer.

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“Artificial intelligence is now one of the fastest-growing areas in all of science and one of the most talked-about topics in society.” VOA

Google AI model was trained and tuned on a representative data set comprised of de-identified mammograms from more than 76,000 women in the UK and more than 15,000 women in the US, to see if it could learn to spot signs of breast cancer in the scans.

The model was then evaluated on a separate de-identified data set of more than 25,000 women in the UK and over 3,000 women in the US.

“In this evaluation, our system produced a 5.7 per cent reduction of false positives in the US, and a 1.2 per cent reduction in the UK. It produced a 9.4 per cent reduction in false negatives in the US, and a 2.7 per cent reduction in the UK,” informed Google.

The researchers then trained the AI model only on the data from the women in the UK and then evaluated it on the data set from women in the US.

In this separate experiment, there was a 3.5 per cent reduction in false positives and an 8.1 per cent reduction in false negatives, “showing the model’s potential to generalize to new clinical settings while still performing at a higher level than experts”.

Notably, when making its decisions, the model received less information than human experts did.

The human experts (in line with routine practice) had access to patient histories and prior mammograms, while the model only processed the most recent anonymized mammogram with no extra information.

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Despite working from these X-ray images alone, the model surpassed individual experts in accurately identifying breast cancer.

This work, said Google, is the latest strand of its research looking into detection and diagnosis of breast cancer, not just within the scope of radiology, but also pathology.

“We’re looking forward to working with our partners in the coming years to translate our machine learning research into tools that benefit clinicians and patients,” said the tech giant. (IANS)