What happens when machine learning — computer programs that absorb^(a) new information and then change how they make decisions — causes investment losses, a car accident, or a wrong cancer diagnosis?

The big difference between machine learning and previous^(b) digital technologies is the ability to independently make progressively complex decisions — such as which financial products to trade — and continuously adapt in response to new data. But these algorithms don’t always make ethical or precise^(c) choices.

The imperfections of machine learning raise another important challenge: risks deriving from things that aren’t under the control of a specific business or user. Ordinarily, it’s possible to draw on reliable evidence to reconstruct the circumstances that led to an accident. But because machine learning is typically inserted within a complex system, it will often be unclear what led to a breakdown — which party, or “agent”^(d) (for example, the algorithm developer, the system deployer, or a partner), was responsible for an error and whether there was a problem with the algorithm.

Actually,^(e) accidents or illicit decisions can occur even without negligence on anyone’s part — as there is simply always the possibility of an inaccurate decision.

(https://hbr.org. Adaptado)

Leia o texto para responder às questões de números 17 a 21.

What happens when machine learning — computer programs that absorb new information and then change how they make decisions — causes investment losses, a car accident, or a wrong cancer diagnosis?

The big difference between machine learning and previous digital technologies is the ability to independently make progressively complex decisions — such as which financial products to trade — and continuously adapt in response to new data. But these algorithms don’t always make ethical or precise choices.

The imperfections of machine learning raise another important challenge: risks deriving from things that aren’t under the control of a specific business or user. Ordinarily, it’s possible to draw on reliable evidence to reconstruct the circumstances that led to an accident. But because machine learning is typically inserted within a complex system, it will often be unclear what led to a breakdown — which party, or “agent” (for example, the algorithm developer, the system deployer, or a partner), was responsible for an error and whether there was a problem with the algorithm.

Actually, accidents or illicit decisions can occur even without negligence on anyone’s part — as there is simply always the possibility of an inaccurate decision.

(https://hbr.org. Adaptado)

Leia o texto para responder às questões de números 17 a 21.

What happens when machine learning — computer programs that absorb new information and then change how they make decisions — causes investment losses, a car accident, or a wrong cancer diagnosis?

The big difference between machine learning and previous digital technologies is the ability to independently make progressively complex decisions — such as which financial products to trade — and continuously adapt in response to new data. But these algorithms don’t always make ethical or precise choices.

The imperfections of machine learning raise another important challenge: risks deriving from things that aren’t under the control of a specific business or user. Ordinarily, it’s possible to draw on reliable evidence to reconstruct the circumstances that led to an accident. But because machine learning is typically inserted within a complex system, it will often be unclear what led to a breakdown — which party, or “agent” (for example, the algorithm developer, the system deployer, or a partner), was responsible for an error and whether there was a problem with the algorithm.

Actually, accidents or illicit decisions can occur even without negligence on anyone’s part — as there is simply always the possibility of an inaccurate decision.

(https://hbr.org. Adaptado)

Leia o texto para responder às questões de números 17 a 21.

What happens when machine learning — computer programs that absorb new information and then change how they make decisions — causes investment losses, a car accident, or a wrong cancer diagnosis?

The big difference between machine learning and previous digital technologies is the ability to independently make progressively complex decisions — such as which financial products to trade — and continuously adapt in response to new data. But these algorithms don’t always make ethical or precise choices.

The imperfections of machine learning raise another important challenge: risks deriving from things that aren’t under the control of a specific business or user. Ordinarily, it’s possible to draw on reliable evidence to reconstruct the circumstances that led to an accident. But because machine learning is typically inserted within a complex system, it will often be unclear what led to a breakdown — which party, or “agent” (for example, the algorithm developer, the system deployer, or a partner), was responsible for an error and whether there was a problem with the algorithm.

Actually, accidents or illicit decisions can occur even without negligence on anyone’s part — as there is simply always the possibility of an inaccurate decision.

(https://hbr.org. Adaptado)

Leia o texto para responder às questões de números 17 a 21.

What happens when machine learning — computer programs that absorb new information and then change how they make decisions — causes investment losses, a car accident, or a wrong cancer diagnosis?

The big difference between machine learning and previous digital technologies is the ability to independently make progressively complex decisions — such as which financial products to trade — and continuously adapt in response to new data. But these algorithms don’t always make ethical or precise choices.

The imperfections of machine learning raise another important challenge: risks deriving from things that aren’t under the control of a specific business or user. Ordinarily, it’s possible to draw on reliable evidence to reconstruct the circumstances that led to an accident. But because machine learning is typically inserted within a complex system, it will often be unclear what led to a breakdown — which party, or “agent” (for example, the algorithm developer, the system deployer, or a partner), was responsible for an error and whether there was a problem with the algorithm.

Actually, accidents or illicit decisions can occur even without negligence on anyone’s part — as there is simply always the possibility of an inaccurate decision.

(https://hbr.org. Adaptado)

Leia o texto para responder às questões de números 13 a 16.

Information security (sometimes referred to as InfoSec) covers the tools and processes that organizations use to protect information. This includes policy settings that prevent unauthorized people from accessing business or personal information. InfoSec is a growing field that covers a wide range of fields, from network and infrastructure security to testing and auditing.

Information security protects sensitive information from unauthorized activities, including inspection, modification, recording, and any disruption or destruction. The goal is to ensure the safety and privacy of critical data such as customer account details, financial data or intellectual property.

The consequences of security incidents include theft of private information, data tampering and data deletion. Attacks can disrupt work processes and damage a company’s reputation, and also have a tangible cost. Organizations must allocate funds for security and ensure that they are ready to detect, respond to, and proactively prevent, attacks such as phishing, malware, viruses, malicious insiders, and ransomware.

(https://www.imperva.com. Adaptado)

Leia o texto para responder às questões de números 13 a 16.

Information security (sometimes referred to as InfoSec) covers the tools and processes that organizations use to protect information. This includes policy settings that prevent unauthorized people from accessing business or personal information. InfoSec is a growing field that covers a wide range of fields, from network and infrastructure security to testing and auditing.

Information security protects sensitive information from unauthorized activities, including inspection, modification, recording, and any disruption or destruction. The goal is to ensure the safety and privacy of critical data such as customer account details, financial data or intellectual property.

The consequences of security incidents include theft of private information, data tampering and data deletion. Attacks can disrupt work processes and damage a company’s reputation, and also have a tangible cost. Organizations must allocate funds for security and ensure that they are ready to detect, respond to, and proactively prevent, attacks such as phishing, malware, viruses, malicious insiders, and ransomware.

(https://www.imperva.com. Adaptado)

Information security (sometimes referred to as InfoSec) covers the tools and processes that organizations use to protect information. This includes policy settings that prevent unauthorized people from accessing business or personal information. InfoSec is a growing field that covers a wide range of fields, from network and infrastructure security^(a) to testing and auditing.

Information security protects sensitive information from unauthorized activities, including inspection, modification, recording, and any disruption or destruction. The goal is to ensure the safety and privacy of critical data^(b) such as customer account details,^(c) financial data or intellectual property.^(d)

The consequences of security incidents include theft of private information, data tampering and data deletion.^(e)

Attacks can disrupt work processes and damage a company’s reputation, and also have a tangible cost. Organizations must allocate funds for security and ensure that they are ready to detect, respond to, and proactively prevent, attacks such as phishing, malware, viruses, malicious insiders, and ransomware.

(https://www.imperva.com. Adaptado)

Information security (sometimes referred to as InfoSec) covers the tools and processes that organizations use to protect information. This includes policy settings that prevent unauthorized people from accessing business or personal information. InfoSec is a growing field that covers a wide range of fields, from network and infrastructure security to testing and auditing.

Information security protects sensitive information from unauthorized activities, including inspection, modification, recording, and any disruption or destruction. The goal is to ensure the safety and privacy of critical data such as customer account details, financial data or intellectual property.

The consequences of security incidents include theft of private information, data tampering and data deletion.

Attacks can disrupt work processes and damage a company’s reputation, and also have a tangible cost. Organizations must allocate funds for security and ensure that they are ready to detect, respond to, and proactively prevent, attacks such as phishing, malware, viruses, malicious insiders, and ransomware.

(https://www.imperva.com. Adaptado)